From a89faf7b479f1f4b359ba7f7a4791b529da455b0 Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Sun, 21 Apr 2024 22:57:50 -0400
Subject: [PATCH 01/24] Use repl language tag for sample

---
 docs/src/transformers.md | 31 +++++++++++++++----------------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/docs/src/transformers.md b/docs/src/transformers.md
index f03cdb92f..9b868e967 100644
--- a/docs/src/transformers.md
+++ b/docs/src/transformers.md
@@ -193,18 +193,17 @@ K-means clustering algorithm assigns one of three labels 1, 2, 3 to
 the input features of the iris data set and compares them with the
 actual species recorded in the target (not seen by the algorithm).
 
-```julia
-import Random.seed!
-seed!(123)
+```julia-repl
+julia> import Random.seed!
+julia> seed!(123)
 
-X, y = @load_iris;
-KMeans = @load KMeans pkg=ParallelKMeans
-kmeans = KMeans()
-mach = machine(kmeans, X) |> fit!
+julia> X, y = @load_iris;
+julia> KMeans = @load KMeans pkg=ParallelKMeans
+julia> kmeans = KMeans()
+julia> mach = machine(kmeans, X) |> fit!
 
-# transforming:
-Xsmall = transform(mach);
-selectrows(Xsmall, 1:4) |> pretty
+julia> # transforming:
+julia> Xsmall = transform(mach);
 julia> selectrows(Xsmall, 1:4) |> pretty
 ┌─────────────────────┬────────────────────┬────────────────────┐
 │ x1                  │ x2                 │ x3                 │
@@ -217,10 +216,10 @@ julia> selectrows(Xsmall, 1:4) |> pretty
 │ 0.26919199999998966 │ 26.28656804733727  │ 11.64392098898145  │
 └─────────────────────┴────────────────────┴────────────────────┘
 
-# predicting:
-yhat = predict(mach);
-compare = zip(yhat, y) |> collect;
-compare[1:8]
+julia> # predicting:
+julia> yhat = predict(mach);
+julia> compare = zip(yhat, y) |> collect;
+julia> compare[1:8]
 8-element Array{Tuple{CategoricalValue{Int64,UInt32},CategoricalString{UInt32}},1}:
  (1, "setosa")
  (1, "setosa")
@@ -231,7 +230,7 @@ compare[1:8]
  (1, "setosa")
  (1, "setosa")
 
-compare[51:58]
+julia> compare[51:58]
 8-element Array{Tuple{CategoricalValue{Int64,UInt32},CategoricalString{UInt32}},1}:
  (2, "versicolor")
  (3, "versicolor")
@@ -242,7 +241,7 @@ compare[51:58]
  (3, "versicolor")
  (3, "versicolor")
 
-compare[101:108]
+julia> compare[101:108]
 8-element Array{Tuple{CategoricalValue{Int64,UInt32},CategoricalString{UInt32}},1}:
  (2, "virginica")
  (3, "virginica")

From 8e45385760e5a87136afd6f3442287514c6b459b Mon Sep 17 00:00:00 2001
From: Abhro <5664668+abhro@users.noreply.github.com>
Date: Mon, 22 Apr 2024 17:00:02 -0400
Subject: [PATCH 02/24] Update language tags for code samples

---
 docs/src/about_mlj.md                     | 10 +--
 docs/src/common_mlj_workflows.md          | 13 ++--
 docs/src/controlling_iterative_models.md  |  1 -
 docs/src/evaluating_model_performance.md  | 12 ++--
 docs/src/getting_started.md               | 10 +--
 docs/src/internals.md                     |  2 +-
 docs/src/learning_curves.md               |  2 +-
 docs/src/learning_networks.md             | 79 +++++++++++------------
 docs/src/linear_pipelines.md              |  2 +-
 docs/src/loading_model_code.md            |  8 +--
 docs/src/machines.md                      |  4 +-
 docs/src/mlj_cheatsheet.md                | 52 +++++++++------
 docs/src/model_search.md                  | 14 ++--
 docs/src/preparing_data.md                | 36 +++++------
 docs/src/simple_user_defined_models.md    | 33 +++-------
 docs/src/weights.md                       |  4 +-
 docs/src/working_with_categorical_data.md | 34 +++++-----
 17 files changed, 155 insertions(+), 161 deletions(-)

diff --git a/docs/src/about_mlj.md b/docs/src/about_mlj.md
index a33daba26..bf63f8dcc 100755
--- a/docs/src/about_mlj.md
+++ b/docs/src/about_mlj.md
@@ -110,7 +110,7 @@ X, y = @load_reduced_ames;
 Evaluating the "self-tuning" pipeline model's performance using 5-fold
 cross-validation (implies multiple layers of nested resampling):
 
-```julia
+```julia-repl
 julia> evaluate(self_tuning_pipe, X, y,
                 measures=[l1, l2],
                 resampling=CV(nfolds=5, rng=123),
@@ -229,19 +229,19 @@ installed in a new
 [environment](https://julialang.github.io/Pkg.jl/v1/environments/) to
 avoid package conflicts. You can do this with
 
-```julia
+```julia-repl
 julia> using Pkg; Pkg.activate("my_MLJ_env", shared=true)
 ```
 
 Installing MLJ is also done with the package manager:
 
-```julia
+```julia-repl
 julia> Pkg.add("MLJ")
 ```
 
 **Optional:** To test your installation, run
 
-```julia
+```julia-repl
 julia> Pkg.test("MLJ")
 ```
 
@@ -252,7 +252,7 @@ environment to make model-specific code available. This
 happens automatically when you use MLJ's interactive load command
 `@iload`, as in
 
-```julia
+```julia-repl
 julia> Tree = @iload DecisionTreeClassifier # load type
 julia> tree = Tree() # instance
 ```
diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index 2b7cfaec9..5c684fcc1 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -14,9 +14,9 @@ channing = (Sex = rand(["Male","Female"], 462),
 coerce!(channing, :Sex => Multiclass)
 ```
 
-```julia
-import RDatasets
-channing = RDatasets.dataset("boot", "channing")
+```julia-repl
+julia> import RDatasets
+julia> channing = RDatasets.dataset("boot", "channing")
 
 julia> first(channing, 4)
 4×5 DataFrame
@@ -40,7 +40,7 @@ Horizontally splitting data and shuffling rows.
 Here `y` is the `:Exit` column and `X` everything else:
 
 ```@example workflows
-y, X =  unpack(channing, ==(:Exit), rng=123);
+y, X = unpack(channing, ==(:Exit), rng=123);
 nothing # hide
 ```
 
@@ -514,7 +514,8 @@ curve = learning_curve(mach,
 
 ```julia
 using Plots
-plot(curve.parameter_values, curve.measurements, xlab=curve.parameter_name, xscale=curve.parameter_scale)
+plot(curve.parameter_values, curve.measurements,
+     xlab=curve.parameter_name, xscale=curve.parameter_scale)
 ```
 
 ![](img/workflows_learning_curve.png)
@@ -534,7 +535,7 @@ curve = learning_curve(mach,
 
 ```julia
 plot(curve.parameter_values, curve.measurements,
-xlab=curve.parameter_name, xscale=curve.parameter_scale)
+     xlab=curve.parameter_name, xscale=curve.parameter_scale)
 ```
 
 ![](img/workflows_learning_curves.png)
diff --git a/docs/src/controlling_iterative_models.md b/docs/src/controlling_iterative_models.md
index 77924219d..e0cabdb40 100644
--- a/docs/src/controlling_iterative_models.md
+++ b/docs/src/controlling_iterative_models.md
@@ -253,7 +253,6 @@ In the code, `wrapper` is an object that wraps the training machine
 in this example).
 
 ```julia
-
 import IterationControl # or MLJ.IterationControl
 
 struct IterateFromList
diff --git a/docs/src/evaluating_model_performance.md b/docs/src/evaluating_model_performance.md
index 448283c57..c378b46ec 100644
--- a/docs/src/evaluating_model_performance.md
+++ b/docs/src/evaluating_model_performance.md
@@ -27,7 +27,7 @@ using MLJ
 X = (a=rand(12), b=rand(12), c=rand(12));
 y = X.a + 2X.b + 0.05*rand(12);
 model = (@load RidgeRegressor pkg=MultivariateStats verbosity=0)()
-cv=CV(nfolds=3)
+cv = CV(nfolds=3)
 evaluate(model, X, y, resampling=cv, measure=l2, verbosity=0)
 ```
 
@@ -51,8 +51,8 @@ Multiple measures are specified as a vector:
 evaluate!(
     mach,
     resampling=cv,
-    measures=[l1, rms, rmslp1], 
-	verbosity=0,
+    measures=[l1, rms, rmslp1],
+    verbosity=0,
 )
 ```
 
@@ -70,7 +70,7 @@ evaluate!(
     mach,
     resampling=CV(nfolds=3),
     measure=[l2, rsquared],
-    weights=weights, 
+    weights=weights,
 )
 ```
 
@@ -91,8 +91,8 @@ fold1 = 1:6; fold2 = 7:12;
 evaluate!(
     mach,
     resampling = [(fold1, fold2), (fold2, fold1)],
-    measures=[l1, l2], 
-	verbosity=0,
+    measures=[l1, l2],
+    verbosity=0,
 )
 ```
 
diff --git a/docs/src/getting_started.md b/docs/src/getting_started.md
index 8a44c8c6a..6ddf5f89f 100644
--- a/docs/src/getting_started.md
+++ b/docs/src/getting_started.md
@@ -5,14 +5,14 @@ For an outline of MLJ's **goals** and **features**, see
 
 This page introduces some MLJ basics, assuming some familiarity with
 machine learning. For a complete list of other MLJ learning resources,
-see [Learning MLJ](@ref). 
+see [Learning MLJ](@ref).
 
 MLJ collects together the functionality provided by mutliple packages. To learn how to
 install components separately, run `using MLJ; @doc MLJ`.
 
 This section introduces only the most basic MLJ operations and
 concepts. It assumes MLJ has been successfully installed. See
-[Installation](@ref) if this is not the case. 
+[Installation](@ref) if this is not the case.
 
 
 ```@setup doda
@@ -31,7 +31,7 @@ column vectors:
 ```@repl doda
 using MLJ
 iris = load_iris();
-selectrows(iris, 1:3)  |> pretty
+selectrows(iris, 1:3) |> pretty
 schema(iris)
 ```
 
@@ -114,8 +114,8 @@ computing the mode of each prediction):
 ```@repl doda
 evaluate(tree, X, y,
          resampling=CV(shuffle=true),
-                 measures=[log_loss, accuracy],
-                 verbosity=0)
+         measures=[log_loss, accuracy],
+         verbosity=0)
 ```
 
 Under the hood, `evaluate` calls lower level functions `predict` or
diff --git a/docs/src/internals.md b/docs/src/internals.md
index f31d4ede8..47bdf5335 100755
--- a/docs/src/internals.md
+++ b/docs/src/internals.md
@@ -49,7 +49,7 @@ function fit!(mach::Machine; rows=nothing, force=false, verbosity=1)
     end
 
     rows_have_changed  = (!isdefined(mach, :previous_rows) ||
-	    rows != mach.previous_rows)
+            rows != mach.previous_rows)
 
     args = [MLJ.selectrows(arg, rows) for arg in mach.args]
 
diff --git a/docs/src/learning_curves.md b/docs/src/learning_curves.md
index 42847171a..19f011dc0 100644
--- a/docs/src/learning_curves.md
+++ b/docs/src/learning_curves.md
@@ -48,7 +48,7 @@ used using `rngs=...` (an integer automatically generates the number
 specified):
 
 ```@example hooking
-atom.lambda= 7.3
+atom.lambda = 7.3
 r_n = range(ensemble, :n, lower=1, upper=50)
 curves = MLJ.learning_curve(mach;
                             range=r_n,
diff --git a/docs/src/learning_networks.md b/docs/src/learning_networks.md
index 46e688941..84007be3f 100644
--- a/docs/src/learning_networks.md
+++ b/docs/src/learning_networks.md
@@ -320,18 +320,18 @@ has the same signature as `MLJModelInterface.fit`):
 import MLJBase
 function MLJBase.prefit(composite::CompositeA, verbosity, X, y)
 
-        # the learning network from above:
-        Xs = source(X)
-        ys = source(y)
-        mach1 = machine(:preprocessor, Xs)
-        x = transform(mach1, Xs)
-        mach2 = machine(:classifier, x, ys)
-        yhat = predict(mach2, x)
-
-        verbosity > 0 && @info "I'm a noisy fellow!"
-
-        # return "learning network interface":
-        return (; predict=yhat)
+    # the learning network from above:
+    Xs = source(X)
+    ys = source(y)
+    mach1 = machine(:preprocessor, Xs)
+    x = transform(mach1, Xs)
+    mach2 = machine(:classifier, x, ys)
+    yhat = predict(mach2, x)
+
+    verbosity > 0 && @info "I'm a noisy fellow!"
+
+    # return "learning network interface":
+    return (; predict=yhat)
 end
 ```
 
@@ -594,10 +594,10 @@ using MLJ
 import MLJBase
 
 mutable struct CompositeE <: DeterministicNetworkComposite
-        clusterer     # `:kmeans` or `:kmedoids`
-        k::Int        # number of clusters
-        solver        # a ridge regression parameter we want to expose
-        c::Float64    # a "coupling" coefficient
+    clusterer     # `:kmeans` or `:kmedoids`
+    k::Int        # number of clusters
+    solver        # a ridge regression parameter we want to expose
+    c::Float64    # a "coupling" coefficient
 end
 ```
 
@@ -610,26 +610,26 @@ KMedoids = @load KMedoids pkg=Clustering verbosity=0
 
 function MLJBase.prefit(composite::CompositeE, verbosity, X, y)
 
-        Xs = source(X)
-        ys = source(y)
+    Xs = source(X)
+    ys = source(y)
 
-        k = composite.k
-        solver = composite.solver
-        c = composite.c
+    k = composite.k
+    solver = composite.solver
+    c = composite.c
 
-        clusterer = composite.clusterer == :kmeans ? KMeans(; k) : KMedoids(; k)
-        mach1 = machine(clusterer, Xs)
-        Xsmall = transform(mach1, Xs)
+    clusterer = composite.clusterer == :kmeans ? KMeans(; k) : KMedoids(; k)
+    mach1 = machine(clusterer, Xs)
+    Xsmall = transform(mach1, Xs)
 
-        # the coupling - ridge regularization depends on the number of
-        # clusters `k` and the coupling coefficient `c`:
-        lambda = exp(-c/k)
+    # the coupling - ridge regularization depends on the number of
+    # clusters `k` and the coupling coefficient `c`:
+    lambda = exp(-c/k)
 
-        ridge = RidgeRegressor(; lambda, solver)
-        mach2 = machine(ridge, Xsmall, ys)
-        yhat = predict(mach2, Xsmall)
+    ridge = RidgeRegressor(; lambda, solver)
+    mach2 = machine(ridge, Xsmall, ys)
+    yhat = predict(mach2, Xsmall)
 
-        return (predict=yhat,)
+    return (predict=yhat,)
 end
 ```
 
@@ -748,20 +748,19 @@ Q = @node sqrt(Z)
 (so that `Q() == 4`). Here's a more complicated application of `@node` to row-shuffle a
 table:
 
-```julia
-using Random
-X = (x1 = [1, 2, 3, 4, 5],
-         x2 = [:one, :two, :three, :four, :five])
-rows(X) = 1:nrows(X)
+```julia-repl
+julia> using Random
+julia> X = (x1 = [1, 2, 3, 4, 5],
+            x2 = [:one, :two, :three, :four, :five])
+julia> rows(X) = 1:nrows(X)
 
-Xs = source(X)
-rs  = @node rows(Xs)
-W = @node selectrows(Xs, @node shuffle(rs))
+julia> Xs = source(X)
+julia> rs = @node rows(Xs)
+julia> W = @node selectrows(Xs, @node shuffle(rs))
 
 julia> W()
 (x1 = [5, 1, 3, 2, 4],
  x2 = Symbol[:five, :one, :three, :two, :four],)
-
 ```
 
 **Important.** An argument not in global scope is assumed by `@node` to be a node or
diff --git a/docs/src/linear_pipelines.md b/docs/src/linear_pipelines.md
index 9ab9fc283..394e473be 100644
--- a/docs/src/linear_pipelines.md
+++ b/docs/src/linear_pipelines.md
@@ -29,7 +29,7 @@ model type `KNNRegressor` assumes the features are all
   `X` with `coerce(X, :age=>Continuous)`
 - standardizing continuous features and one-hot encoding the
   `Multiclass` features using the `ContinuousEncoder` model
-  
+
 However, we can avoid separately applying these preprocessing steps
 (two of which require `fit!` steps) by combining them with the
 supervised `KKNRegressor` model in a new *pipeline* model, using
diff --git a/docs/src/loading_model_code.md b/docs/src/loading_model_code.md
index f3ce17a1e..850e5a262 100644
--- a/docs/src/loading_model_code.md
+++ b/docs/src/loading_model_code.md
@@ -32,7 +32,7 @@ provided by the
 package. Then, to determine which package provides the MLJ interface
 you call `load_path`:
 
-```julia
+```julia-repl
 julia> load_path("DecisionTreeClassifier", pkg="DecisionTree")
 "MLJDecisionTreeInterface.DecisionTreeClassifier"
 ```
@@ -41,7 +41,7 @@ In this case, we see that the package required is
 MLJDecisionTreeInterface.jl. If this package is not in `my_env` (do
 `Pkg.status()` to check) you add it by running
 
-```julia
+```julia-repl
 julia> Pkg.add("MLJDecisionTreeInterface");
 ```
 
@@ -49,14 +49,14 @@ So long as `my_env` is the active environment, this action need never
 be repeated (unless you run `Pkg.rm("MLJDecisionTreeInterface")`). You
 are now ready to instantiate a decision tree classifier:
 
-```julia
+```julia-repl
 julia> Tree = @load DecisionTree pkg=DecisionTree
 julia> tree = Tree()
 ```
 
 which is equivalent to
 
-```julia
+```julia-repl
 julia> import MLJDecisionTreeInterface.DecisionTreeClassifier
 julia> Tree = MLJDecisionTreeInterface.DecisionTreeClassifier
 julia> tree = Tree()
diff --git a/docs/src/machines.md b/docs/src/machines.md
index 68eb9cddc..7ad56c935 100644
--- a/docs/src/machines.md
+++ b/docs/src/machines.md
@@ -33,7 +33,7 @@ Generally, changing a hyperparameter triggers retraining on calls to
 subsequent `fit!`:
 
 ```@repl machines
-forest.bagging_fraction=0.5
+forest.bagging_fraction = 0.5
 fit!(mach, verbosity=2);
 ```
 
@@ -41,7 +41,7 @@ However, for this iterative model, increasing the iteration parameter
 only adds models to the existing ensemble:
 
 ```@repl machines
-forest.n=15
+forest.n = 15
 fit!(mach, verbosity=2);
 ```
 
diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 397c7690b..1c0573d42 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -34,14 +34,15 @@ With additional conditions:
 models() do model
     matching(model, X, y) &&
     model.prediction_type == :probabilistic &&
-        model.is_pure_julia
+    model.is_pure_julia
 end
 ```
 
-`Tree = @load DecisionTreeClassifier pkg=DecisionTree` imports "DecisionTreeClassifier" type and binds it to `Tree`
-`tree = Tree()` to instantiate a `Tree`. 
+`Tree = @load DecisionTreeClassifier pkg=DecisionTree` imports "DecisionTreeClassifier" type and binds it to `Tree`.
 
-`tree2  = Tree(max_depth=2)` instantiates a tree with different hyperparameter
+`tree = Tree()` to instantiate a `Tree`.
+
+`tree2 = Tree(max_depth=2)` instantiates a tree with different hyperparameter
 
 `Ridge = @load RidgeRegressor pkg=MultivariateStats` imports a type for a model provided by multiple packages
 
@@ -96,20 +97,26 @@ y, X =  unpack(channing,
 
 Splitting row indices into train/validation/test, with seeded shuffling:
 
-`train, valid, test = partition(eachindex(y), 0.7, 0.2, rng=1234)` for 70:20:10 ratio
+```julia-repl
+julia> train, valid, test = partition(eachindex(y), 0.7, 0.2, rng=1234) # for 70:20:10 ratio
+```
 
 For a stratified split:
 
-`train, test = partition(eachindex(y), 0.8, stratify=y)`
+```julia-repl
+julia> train, test = partition(eachindex(y), 0.8, stratify=y)
+```
 
 Split a table or matrix `X`, instead of indices:
 
-`Xtrain, Xvalid, Xtest = partition(X, 0.5, 0.3, rng=123)` 
+```julia-repl
+julia> Xtrain, Xvalid, Xtest = partition(X, 0.5, 0.3, rng=123)
+```
 
 Getting data from [OpenML](https://www.openml.org):
-
-`table = OpenML.load(91)`
-
+```julia-repl
+julia> table = OpenML.load(91)
+```
 Creating synthetic classification data:
 
 `X, y = make_blobs(100, 2)` (also: `make_moons`, `make_circles`)
@@ -121,12 +128,17 @@ Creating synthetic regression data:
 ## Machine construction
 
 Supervised case:
-
-`model = KNNRegressor(K=1)` and `mach = machine(model, X, y)`
+```julia-repl
+julia> model = KNNRegressor(K=1)
+julia> mach = machine(model, X, y)
+```
 
 Unsupervised case:
 
-`model = OneHotEncoder()` and `mach = machine(model, X)`
+```julia-repl
+julia> model = OneHotEncoder()
+julia> mach = machine(model, X)
+```
 
 ## Fitting
 
@@ -283,7 +295,7 @@ Externals include: `PCA` (in MultivariateStats), `KMeans`, `KMedoids` (in Cluste
 
 ## Pipelines
 
-`pipe = (X -> coerce(X, :height=>Continuous)) |> OneHotEncoder |> KNNRegressor(K=3)` 
+`pipe = (X -> coerce(X, :height=>Continuous)) |> OneHotEncoder |> KNNRegressor(K=3)`
 
 Unsupervised:
 
@@ -311,9 +323,10 @@ Supervised, with final node `yhat` returning point predictions:
 ```julia
 @from_network machine(Deterministic(), Xs, ys; predict=yhat) begin
     mutable struct Composite
-	    reducer=network_pca
-		regressor=network_knn
+        reducer=network_pca
+        regressor=network_knn
     end
+end
 ```
 
 Here `network_pca` and `network_knn` are models appearing in the
@@ -327,6 +340,7 @@ Supervised, with `yhat` final node returning probabilistic predictions:
         reducer=network_pca
         classifier=network_tree
     end
+end
 ```
 
 Unsupervised, with final node `Xout`:
@@ -334,8 +348,8 @@ Unsupervised, with final node `Xout`:
 ```julia
 @from_network machine(Unsupervised(), Xs; transform=Xout) begin
     mutable struct Composite
-	    reducer1=network_pca
-		reducer2=clusterer
+        reducer1=network_pca
+        reducer2=clusterer
     end
 end
-```UnivariateTimeTypeToContinuous
+```
diff --git a/docs/src/model_search.md b/docs/src/model_search.md
index 1f3cfff49..b6e344fa7 100644
--- a/docs/src/model_search.md
+++ b/docs/src/model_search.md
@@ -5,7 +5,7 @@ properties, without loading all the packages containing model code. In
 turn, this allows one to efficiently find all models solving a given
 machine learning task. The task itself is specified with the help of
 the `matching` method, and the search executed with the `models`
-methods, as detailed below. 
+methods, as detailed below.
 
 For commonly encountered problems with model search, see also
 [Preparing Data](@ref).
@@ -33,27 +33,29 @@ info("PCA")
 So a "model" in the present context is just a named tuple containing
 metadata, and not an actual model type or instance. If two models with
 the same name occur in different packages, the package name must be
-specified, as in `info("LinearRegressor", pkg="GLM")`. 
+specified, as in `info("LinearRegressor", pkg="GLM")`.
 
 Model document strings can be retreived, without importing the
 defining code, using the `doc` function:
 
-```
+```julia
 doc("DecisionTreeClassifier", pkg="DecisionTree")
 ```
 
 ## General model queries
 
-We list all models (named tuples) using `models()`, and list the models for which code is  already loaded with `localmodels()`:
+We list all models (named tuples) using `models()`, and list the models for
+which code is  already loaded with `localmodels()`:
 
 ```@repl tokai
 localmodels()
 localmodels()[2]
 ```
 
-One can search for models containing specified strings or regular expressions in their `docstring` attributes, as in
+One can search for models containing specified strings or regular expressions in
+their `docstring` attributes, as in
 
-```@repl tokai 
+```@repl tokai
 models("forest")
 ```
 
diff --git a/docs/src/preparing_data.md b/docs/src/preparing_data.md
index e87260707..c796f96b2 100644
--- a/docs/src/preparing_data.md
+++ b/docs/src/preparing_data.md
@@ -2,9 +2,9 @@
 
 ## Splitting data
 
-MLJ has two tools for splitting data. To split data *vertically* (that
-is, to split by observations) use [`partition`](@ref). This is commonly applied to a
-vector of observation *indices*, but can also be applied to datasets
+MLJ has two tools for splitting data. To split data *vertically* (that is,
+to split by observations) use [`partition`](@ref). This is commonly applied to
+a vector of observation *indices*, but can also be applied to datasets
 themselves, provided they are vectors, matrices or tables.
 
 To split tabular data *horizontally* (i.e., break up a table based on
@@ -39,18 +39,17 @@ models(matching(X, y))
 
 Or are unsure about the source of the following warning:
 
-```julia
-Tree = @load DecisionTreeRegressor pkg=DecisionTree verbosity=0
-tree = Tree();
-julia> machine(tree, X, y)
+```julia-repl
+julia> Tree = @load DecisionTreeRegressor pkg=DecisionTree verbosity=0
+julia> tree = Tree();
 
 julia> machine(tree, X, y)
-┌ Warning: The scitype of `X`, in `machine(model, X, ...)` is incompatible with `model=DecisionTreeRegressor @378`:                                                                
+┌ Warning: The scitype of `X`, in `machine(model, X, ...)` is incompatible with `model=DecisionTreeRegressor @378`:
 │ scitype(X) = Table{Union{AbstractVector{Continuous}, AbstractVector{Count}, AbstractVector{Textual}, AbstractVector{Union{Missing, Textual}}}}
 │ input_scitype(model) = Table{var"#s46"} where var"#s46"<:Union{AbstractVector{var"#s9"} where var"#s9"<:Continuous, AbstractVector{var"#s9"} where var"#s9"<:Count, AbstractVector{var"#s9"} where var"#s9"<:OrderedFactor}.
 └ @ MLJBase ~/Dropbox/Julia7/MLJ/MLJBase/src/machines.jl:103
 Machine{DecisionTreeRegressor,…} @198 trained 0 times; caches data
-  args: 
+  args:
     1:  Source @628 ⏎ `Table{Union{AbstractVector{Continuous}, AbstractVector{Count}, AbstractVector{Textual}, AbstractVector{Union{Missing, Textual}}}}`
     2:  Source @544 ⏎ `AbstractVector{Continuous}`
 ```
@@ -75,16 +74,14 @@ intended scientific interpretation. If `height` in the above example
 is intended to be `Continuous`, `mark` is supposed to be
 `OrderedFactor`, and `admitted` a (binary) `Multiclass`, then we can
 do
-        
-  
+
 ```@example poot
 X_coerced = coerce(X, :height=>Continuous, :mark=>OrderedFactor, :admitted=>Multiclass);
 schema(X_coerced)
 ```
 
 **Data transformations:** We carry out conventional data
-transformations, such as missing value imputation and feature
-encoding:
+transformations, such as missing value imputation and feature encoding:
   
 ```@example poot
 imputer = FillImputer()
@@ -123,18 +120,17 @@ Also relevant is the section, [Working with Categorical Data](@ref).
 
 ## Data transformation
 
-MLJ's Built-in transformers are documented at [Transformers and Other Unsupervised Models](@ref). The most relevant in the present context
-  are: [`ContinuousEncoder`](@ref), [`OneHotEncoder`](@ref),
-  [`FeatureSelector`](@ref) and [`FillImputer`](@ref). A Gaussian
-  mixture models imputer is provided by BetaML, which can be loaded
-       with
+MLJ's Built-in transformers are documented at [Transformers and Other Unsupervised Models](@ref).
+The most relevant in the present context are: [`ContinuousEncoder`](@ref),
+[`OneHotEncoder`](@ref), [`FeatureSelector`](@ref) and [`FillImputer`](@ref).
+A Gaussian mixture models imputer is provided by BetaML, which can be loaded with
 
 ```julia
 MissingImputator = @load MissingImputator pkg=BetaML
 ```
 
 [This MLJ
-Workshop](https://github.com/ablaom/MachineLearningInJulia2020), and the "End-to-end
-examples" in [Data Science in Julia
+Workshop](https://github.com/ablaom/MachineLearningInJulia2020), and
+the "End-to-end examples" in [Data Science in Julia
 tutorials](https://alan-turing-institute.github.io/DataScienceTutorials.jl/)
 give further illustrations of data preprocessing in MLJ.
diff --git a/docs/src/simple_user_defined_models.md b/docs/src/simple_user_defined_models.md
index 57590701a..ffc64920b 100755
--- a/docs/src/simple_user_defined_models.md
+++ b/docs/src/simple_user_defined_models.md
@@ -38,9 +38,10 @@ For an unsupervised model, implement `transform` and, optionally,
 
 Here's a quick-and-dirty implementation of a ridge regressor with no intercept:
 
-```julia
+```@example regressor_example
 import MLJBase
 using LinearAlgebra
+MLJBase.color_off() # hide
 
 mutable struct MyRegressor <: MLJBase.Deterministic
     lambda::Float64
@@ -51,32 +52,14 @@ MyRegressor(; lambda=0.1) = MyRegressor(lambda)
 function MLJBase.fit(model::MyRegressor, verbosity, X, y)
     x = MLJBase.matrix(X)                     # convert table to matrix
     fitresult = (x'x + model.lambda*I)\(x'y)  # the coefficients
-    cache=nothing
-    report=nothing
+    cache = nothing
+    report = nothing
     return fitresult, cache, report
 end
 
 # predict uses coefficients to make a new prediction:
 MLJBase.predict(::MyRegressor, fitresult, Xnew) = MLJBase.matrix(Xnew) * fitresult
-```
-
-``` @setup regressor_example
-using MLJ
-import MLJBase
-using LinearAlgebra
-MLJBase.color_off()
-mutable struct MyRegressor <: MLJBase.Deterministic
-    lambda::Float64
-end
-MyRegressor(; lambda=0.1) = MyRegressor(lambda)
-function MLJBase.fit(model::MyRegressor, verbosity, X, y)
-    x = MLJBase.matrix(X)
-    fitresult = (x'x + model.lambda*I)\(x'y)
-    cache=nothing
-    report=nothing
-    return fitresult, cache, report
-end
-MLJBase.predict(::MyRegressor, fitresult, Xnew) = MLJBase.matrix(Xnew) * fitresult
+nothing # hide
 ```
 
 After loading this code, all MLJ's basic meta-algorithms can be applied to `MyRegressor`:
@@ -116,9 +99,9 @@ MLJBase.predict(model::MyClassifier, fitresult, Xnew) =
     [fitresult for r in 1:nrows(Xnew)]
 ```
 
-```julia
-julia> X, y = @load_iris
-julia> mach = fit!(machine(MyClassifier(), X, y))
+```julia-repl
+julia> X, y = @load_iris;
+julia> mach = fit!(machine(MyClassifier(), X, y));
 julia> predict(mach, selectrows(X, 1:2))
 2-element Array{UnivariateFinite{String,UInt32,Float64},1}:
  UnivariateFinite(setosa=>0.333, versicolor=>0.333, virginica=>0.333)
diff --git a/docs/src/weights.md b/docs/src/weights.md
index 3789faf21..f17cf07b6 100644
--- a/docs/src/weights.md
+++ b/docs/src/weights.md
@@ -2,7 +2,7 @@
 
 In machine learning it is possible to assign each observation an
 independent significance, or *weight*, either in **training** or in
-**performance evaluation**, or both. 
+**performance evaluation**, or both.
 
 There are two kinds of weights in use in MLJ:
 
@@ -11,7 +11,7 @@ There are two kinds of weights in use in MLJ:
 
 - *class weights* refer to dictionaries keyed on the target classes
   (levels) for use in classification problems
-  
+
 
 ## Specifying weights in training
 
diff --git a/docs/src/working_with_categorical_data.md b/docs/src/working_with_categorical_data.md
index ee2dfc221..5ddf5250a 100644
--- a/docs/src/working_with_categorical_data.md
+++ b/docs/src/working_with_categorical_data.md
@@ -65,14 +65,13 @@ see
 [here](https://github.com/JuliaAI/ScientificTypesBase.jl#more-on-the-table-type).)
 
 ```@example hut
-import DataFrames.DataFrame
-X = DataFrame(
-                 name       = ["Siri", "Robo", "Alexa", "Cortana"],
-                 gender     = ["male", "male", "Female", "female"],
-                 likes_soup = [true, false, false, true],
-                 height     = [152, missing, 148, 163],
-                 rating     = [2, 5, 2, 1],
-                 outcome    = ["rejected", "accepted", "accepted", "rejected"])
+import DataFrames: DataFrame
+X = DataFrame( name       = ["Siri", "Robo", "Alexa", "Cortana"],
+               gender     = ["male", "male", "Female", "female"],
+               likes_soup = [true, false, false, true],
+               height     = [152, missing, 148, 163],
+               rating     = [2, 5, 2, 1],
+               outcome    = ["rejected", "accepted", "accepted", "rejected"])
 schema(X)
 ```
 
@@ -102,11 +101,12 @@ levels(X.outcome)
 
 !!! warning "Changing levels of categorical data"
 
-    The order of levels should generally be changed
-    early in your data science workflow and then not again. Similar
-    remarks apply to *adding* levels (which is possible; see the
-    [CategorialArrays.jl documentation](https://juliadata.github.io/CategoricalArrays.jl/stable/)). MLJ supervised and unsupervised models assume levels
-    and their order do not change.
+    The order of levels should generally be changed early in your
+    data science workflow and then not again. Similar remarks apply
+    to *adding* levels (which is possible; see the
+    [CategorialArrays.jl documentation](https://juliadata.github.io/CategoricalArrays.jl/stable/)).
+    MLJ supervised and unsupervised models assume levels and their
+    order do not change.
 
 Coercing all remaining types simultaneously:
 
@@ -157,7 +157,7 @@ or contain previously unseen classes.
 
 ## New or missing levels in production data
 
-!!! warning 
+!!! warning
 
     Unpredictable behavior may result whenever `Finite` categorical data presents in a production set with different classes (levels) from those presented during training
 
@@ -179,7 +179,7 @@ Xproduction == X[2:3,:]
 
 So far, so good. But the following operation throws an error:
 
-```julia
+```julia-repl
 julia> transform(mach, Xproduction) == transform(mach, X[2:3,:])
 ERROR: Found category level mismatch in feature `x`. Consider using `levels!` to ensure fitted and transforming features have the same category levels.
 ```
@@ -287,7 +287,7 @@ came. Use `get(val)` to extract the raw label from a value `val`.
 Despite the distinction that exists between a value (element) and a
 label, the two are the same, from the point of `==` and `in`:
 
-```@julia
+```julia
 v[1] == 'A' # true
 'A' in v    # true
 ```
@@ -338,7 +338,7 @@ d_vec = UnivariateFinite(["no", "yes"], probs, pool=v)
 
 Or, equivalently:
 
-```@julia
+```julia
 d_vec = UnivariateFinite(["no", "yes"], yes_probs, augment=true, pool=v)
 ```
 

From fddc289838753e05f0af3cd0fe72c15b3619182a Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Wed, 24 Apr 2024 00:16:56 -0400
Subject: [PATCH 03/24] Follow blue style in
 docs/src/working_with_categorical_data.md

Co-authored-by: Anthony Blaom, PhD <anthony.blaom@gmail.com>
---
 docs/src/working_with_categorical_data.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/docs/src/working_with_categorical_data.md b/docs/src/working_with_categorical_data.md
index 5ddf5250a..f1506f2f5 100644
--- a/docs/src/working_with_categorical_data.md
+++ b/docs/src/working_with_categorical_data.md
@@ -66,12 +66,14 @@ see
 
 ```@example hut
 import DataFrames: DataFrame
-X = DataFrame( name       = ["Siri", "Robo", "Alexa", "Cortana"],
-               gender     = ["male", "male", "Female", "female"],
-               likes_soup = [true, false, false, true],
-               height     = [152, missing, 148, 163],
-               rating     = [2, 5, 2, 1],
-               outcome    = ["rejected", "accepted", "accepted", "rejected"])
+X = DataFrame(
+    name = ["Siri", "Robo", "Alexa", "Cortana"],
+    gender = ["male", "male", "Female", "female"],
+    likes_soup = [true, false, false, true],
+    height = [152, missing, 148, 163],
+    rating = [2, 5, 2, 1],
+    outcome = ["rejected", "accepted", "accepted", "rejected"],
+)
 schema(X)
 ```
 

From 3d6d15fdc2d1d0e84c0e6c28f511f8f13bb9e09e Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Mon, 29 Apr 2024 17:17:08 -0400
Subject: [PATCH 04/24] Update mlj_cheatsheet.md

---
 docs/src/mlj_cheatsheet.md | 43 ++++++++++++++++++++++----------------
 1 file changed, 25 insertions(+), 18 deletions(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 1c0573d42..a529bb162 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -97,47 +97,52 @@ y, X =  unpack(channing,
 
 Splitting row indices into train/validation/test, with seeded shuffling:
 
-```julia-repl
-julia> train, valid, test = partition(eachindex(y), 0.7, 0.2, rng=1234) # for 70:20:10 ratio
+```julia
+train, valid, test = partition(eachindex(y), 0.7, 0.2, rng=1234) # for 70:20:10 ratio
 ```
 
 For a stratified split:
 
-```julia-repl
-julia> train, test = partition(eachindex(y), 0.8, stratify=y)
+```julia
+train, test = partition(eachindex(y), 0.8, stratify=y)
 ```
 
 Split a table or matrix `X`, instead of indices:
 
-```julia-repl
-julia> Xtrain, Xvalid, Xtest = partition(X, 0.5, 0.3, rng=123)
+```julia
+Xtrain, Xvalid, Xtest = partition(X, 0.5, 0.3, rng=123)
 ```
 
 Getting data from [OpenML](https://www.openml.org):
-```julia-repl
-julia> table = OpenML.load(91)
+```julia
+table = OpenML.load(91)
 ```
 Creating synthetic classification data:
 
-`X, y = make_blobs(100, 2)` (also: `make_moons`, `make_circles`)
+```julia
+X, y = make_blobs(100, 2)
+```
+(also: `make_moons`, `make_circles`)
 
 Creating synthetic regression data:
 
-`X, y = make_regression(100, 2)`
+```julia
+X, y = make_regression(100, 2)
+```
 
 ## Machine construction
 
 Supervised case:
-```julia-repl
-julia> model = KNNRegressor(K=1)
-julia> mach = machine(model, X, y)
+```julia
+model = KNNRegressor(K=1)
+mach = machine(model, X, y)
 ```
 
 Unsupervised case:
 
-```julia-repl
-julia> model = OneHotEncoder()
-julia> mach = machine(model, X)
+```julia
+model = OneHotEncoder()
+mach = machine(model, X)
 ```
 
 ## Fitting
@@ -308,8 +313,10 @@ Concatenation:
 
 ## Define a supervised learning network:
 
-`Xs = source(X)`
-`ys = source(y)`
+```julia
+Xs = source(X)
+ys = source(y)
+```
 
 ... define further nodal machines and nodes ...
 

From ae2815167fb5e219acb3e6ddb0af310d73302f21 Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Mon, 29 Apr 2024 22:50:39 -0400
Subject: [PATCH 05/24] Consistenly use @example in common_mlj_workflows.md

Remove julia> prompts, replace with @example macro
---
 docs/src/common_mlj_workflows.md | 28 +++++++++++-----------------
 1 file changed, 11 insertions(+), 17 deletions(-)

diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index 5c684fcc1..8e31bc71d 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -14,19 +14,13 @@ channing = (Sex = rand(["Male","Female"], 462),
 coerce!(channing, :Sex => Multiclass)
 ```
 
-```julia-repl
-julia> import RDatasets
-julia> channing = RDatasets.dataset("boot", "channing")
+```julia
+import RDatasets
+channing = RDatasets.dataset("boot", "channing")
+```
 
-julia> first(channing, 4)
-4×5 DataFrame
- Row │ Sex   Entry  Exit   Time   Cens
-     │ Cat…  Int32  Int32  Int32  Int32
-─────┼──────────────────────────────────
-   1 │ Male    782    909    127      1
-   2 │ Male   1020   1128    108      1
-   3 │ Male    856    969    113      1
-   4 │ Male    915    957     42      1
+```@example
+first(channing, 4)
 ```
 
 Inspecting metadata, including column scientific types:
@@ -47,10 +41,10 @@ nothing # hide
 Here `y` is the `:Exit` column and `X` everything else except `:Time`:
 
 ```@example workflows
-y, X =  unpack(channing,
-               ==(:Exit),
-               !=(:Time);
-               rng=123);
+y, X = unpack(channing,
+              ==(:Exit),
+              !=(:Time);
+              rng=123);
 scitype(y)
 ```
 
@@ -159,7 +153,7 @@ tree = Tree(min_samples_split=5, max_depth=4)
 
 or
 
-```@julia
+```julia
 tree = (@load DecisionTreeClassifier)()
 tree.min_samples_split = 5
 tree.max_depth = 4

From 9f274adcbf459e7076f69fa0fdc9b5a00a866423 Mon Sep 17 00:00:00 2001
From: Abhro <5664668+abhro@users.noreply.github.com>
Date: Fri, 3 May 2024 10:25:59 -0400
Subject: [PATCH 06/24] Fix @example namespace in common workflows

---
 docs/src/common_mlj_workflows.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index 8e31bc71d..9380ba5a5 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -19,7 +19,7 @@ import RDatasets
 channing = RDatasets.dataset("boot", "channing")
 ```
 
-```@example
+```@example workflows
 first(channing, 4)
 ```
 
@@ -88,7 +88,7 @@ nothing # hide
 Or, if already horizontally split:
 
 ```@example workflows
-(Xtrain, Xtest), (ytrain, ytest)  = partition((X, y), 0.6, multi=true,  rng=123)
+(Xtrain, Xtest), (ytrain, ytest) = partition((X, y), 0.6, multi=true, rng=123)
 ```
 
 
@@ -108,7 +108,7 @@ ms[6]
 ```
 
 ```@example workflows
-models("Tree");
+models("Tree")
 ```
 
 A more refined search:
@@ -224,7 +224,7 @@ Run `measures()` to list all losses and scores and their aliases ("instances").
 Predict on the new data set:
 
 ```@example workflows
-Xnew = (FL = rand(3), RW = rand(3), CL = rand(3), CW = rand(3), BD =rand(3))
+Xnew = (FL = rand(3), RW = rand(3), CL = rand(3), CW = rand(3), BD = rand(3))
 predict(mach, Xnew)      # a vector of distributions
 ```
 

From 367db46a2861cb34b24cadbca9200e0b29334468 Mon Sep 17 00:00:00 2001
From: Abhro <5664668+abhro@users.noreply.github.com>
Date: Fri, 3 May 2024 10:26:35 -0400
Subject: [PATCH 07/24] Break up predicting transformers into separate @example
 blocks

---
 docs/Project.toml        |  1 +
 docs/src/transformers.md | 95 ++++++++++++++++------------------------
 2 files changed, 38 insertions(+), 58 deletions(-)

diff --git a/docs/Project.toml b/docs/Project.toml
index b9718b4ea..d86571e62 100755
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -15,6 +15,7 @@ MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
 NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
+ParallelKMeans = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ScientificTypesBase = "30f210dd-8aff-4c5f-94ba-8e64358c1161"
 StatisticalMeasures = "a19d573c-0a75-4610-95b3-7071388c7541"
diff --git a/docs/src/transformers.md b/docs/src/transformers.md
index 9b868e967..805e715d8 100644
--- a/docs/src/transformers.md
+++ b/docs/src/transformers.md
@@ -193,62 +193,41 @@ K-means clustering algorithm assigns one of three labels 1, 2, 3 to
 the input features of the iris data set and compares them with the
 actual species recorded in the target (not seen by the algorithm).
 
-```julia-repl
-julia> import Random.seed!
-julia> seed!(123)
-
-julia> X, y = @load_iris;
-julia> KMeans = @load KMeans pkg=ParallelKMeans
-julia> kmeans = KMeans()
-julia> mach = machine(kmeans, X) |> fit!
-
-julia> # transforming:
-julia> Xsmall = transform(mach);
-julia> selectrows(Xsmall, 1:4) |> pretty
-┌─────────────────────┬────────────────────┬────────────────────┐
-│ x1                  │ x2                 │ x3                 │
-│ Float64             │ Float64            │ Float64            │
-│ Continuous          │ Continuous         │ Continuous         │
-├─────────────────────┼────────────────────┼────────────────────┤
-│ 0.0215920000000267  │ 25.314260355029603 │ 11.645232464391299 │
-│ 0.19199200000001326 │ 25.882721893491123 │ 11.489658693899486 │
-│ 0.1699920000000077  │ 27.58656804733728  │ 12.674412792260142 │
-│ 0.26919199999998966 │ 26.28656804733727  │ 11.64392098898145  │
-└─────────────────────┴────────────────────┴────────────────────┘
-
-julia> # predicting:
-julia> yhat = predict(mach);
-julia> compare = zip(yhat, y) |> collect;
-julia> compare[1:8]
-8-element Array{Tuple{CategoricalValue{Int64,UInt32},CategoricalString{UInt32}},1}:
- (1, "setosa")
- (1, "setosa")
- (1, "setosa")
- (1, "setosa")
- (1, "setosa")
- (1, "setosa")
- (1, "setosa")
- (1, "setosa")
-
-julia> compare[51:58]
-8-element Array{Tuple{CategoricalValue{Int64,UInt32},CategoricalString{UInt32}},1}:
- (2, "versicolor")
- (3, "versicolor")
- (2, "versicolor")
- (3, "versicolor")
- (3, "versicolor")
- (3, "versicolor")
- (3, "versicolor")
- (3, "versicolor")
-
-julia> compare[101:108]
-8-element Array{Tuple{CategoricalValue{Int64,UInt32},CategoricalString{UInt32}},1}:
- (2, "virginica")
- (3, "virginica")
- (2, "virginica")
- (2, "virginica")
- (2, "virginica")
- (2, "virginica")
- (3, "virginica")
- (2, "virginica")
+```@setup predtrans
+using MLJ
+```
+
+```@example predtrans
+import Random.seed!
+seed!(123)
+
+X, y = @load_iris;
+KMeans = @load KMeans pkg=ParallelKMeans
+kmeans = KMeans()
+mach = machine(kmeans, X) |> fit!
+nothing # hide
+```
+
+Transforming:
+```@example predtrans
+Xsmall = transform(mach);
+selectrows(Xsmall, 1:4) |> pretty
+```
+
+Predicting:
+```@example predtrans
+yhat = predict(mach);
+compare = zip(yhat, y) |> collect;
+```
+
+```@example predtrans
+compare[1:8]
+```
+
+```@example predtrans
+compare[51:58]
+```
+
+```@example predtrans
+compare[101:108]
 ```

From f86b01bf1d0119cbd897109a36637e4a6f20fd9e Mon Sep 17 00:00:00 2001
From: Abhro <5664668+abhro@users.noreply.github.com>
Date: Fri, 3 May 2024 10:27:33 -0400
Subject: [PATCH 08/24] Use @example instead of pre-built repl sample in
 learning_networks.md

---
 docs/src/learning_networks.md | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/docs/src/learning_networks.md b/docs/src/learning_networks.md
index 84007be3f..c275ba543 100644
--- a/docs/src/learning_networks.md
+++ b/docs/src/learning_networks.md
@@ -748,19 +748,17 @@ Q = @node sqrt(Z)
 (so that `Q() == 4`). Here's a more complicated application of `@node` to row-shuffle a
 table:
 
-```julia-repl
-julia> using Random
-julia> X = (x1 = [1, 2, 3, 4, 5],
-            x2 = [:one, :two, :three, :four, :five])
-julia> rows(X) = 1:nrows(X)
-
-julia> Xs = source(X)
-julia> rs = @node rows(Xs)
-julia> W = @node selectrows(Xs, @node shuffle(rs))
-
-julia> W()
-(x1 = [5, 1, 3, 2, 4],
- x2 = Symbol[:five, :one, :three, :two, :four],)
+```@example
+using MLJ, Random
+X = (x1 = [1, 2, 3, 4, 5],
+     x2 = [:one, :two, :three, :four, :five])
+rows(X) = 1:nrows(X)
+
+Xs = source(X)
+rs = @node rows(Xs)
+W = @node selectrows(Xs, @node shuffle(rs))
+
+W()
 ```
 
 **Important.** An argument not in global scope is assumed by `@node` to be a node or

From 211bcf9ac1fb617b36c5e1f7209aa546c32addca Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 21:58:30 -0400
Subject: [PATCH 09/24] Do mechanical fixes of spacing, semicolons, and punc

---
 docs/src/about_mlj.md                     |  14 ++++++--------
 docs/src/adding_models_for_general_use.md |   2 +-
 docs/src/api.md                           |   0
 docs/src/common_mlj_workflows.md          |   6 +++---
 docs/src/controlling_iterative_models.md  |  16 ++++++++--------
 docs/src/evaluating_model_performance.md  |   3 +--
 docs/src/frequently_asked_questions.md    |   0
 docs/src/getting_started.md               |   2 +-
 docs/src/glossary.md                      |   4 ++--
 docs/src/img/two_model_stack.png          | Bin
 docs/src/img/wrapped_ridge.png            | Bin
 docs/src/internals.md                     |   0
 docs/src/learning_networks.md             |   2 +-
 docs/src/loading_model_code.md            |   2 +-
 docs/src/machines.md                      |   8 ++++----
 docs/src/preparing_data.md                |   9 ++++-----
 docs/src/transformers.md                  |   8 ++++----
 docs/src/tuning_models.md                 |   6 +++---
 18 files changed, 39 insertions(+), 43 deletions(-)
 mode change 100755 => 100644 docs/src/about_mlj.md
 mode change 100755 => 100644 docs/src/adding_models_for_general_use.md
 mode change 100755 => 100644 docs/src/api.md
 mode change 100755 => 100644 docs/src/frequently_asked_questions.md
 mode change 100755 => 100644 docs/src/img/two_model_stack.png
 mode change 100755 => 100644 docs/src/img/wrapped_ridge.png
 mode change 100755 => 100644 docs/src/internals.md

diff --git a/docs/src/about_mlj.md b/docs/src/about_mlj.md
old mode 100755
new mode 100644
index 59afc2269..86012b056
--- a/docs/src/about_mlj.md
+++ b/docs/src/about_mlj.md
@@ -1,6 +1,6 @@
 # About MLJ
 
-MLJ (Machine Learning in Julia) is a toolbox written in Julia 
+MLJ (Machine Learning in Julia) is a toolbox written in Julia
 providing a common interface and meta-algorithms for selecting,
 tuning, evaluating, composing and comparing [over 180 machine learning
 models](@ref model_list) written in Julia and other languages. In
@@ -22,8 +22,7 @@ The first code snippet below creates a new Julia environment
 [Installation](@ref) for more on creating a Julia environment for use
 with MLJ.
 
-Julia installation instructions are
-[here](https://julialang.org/downloads/).
+Julia installation instructions are [here](https://julialang.org/downloads/).
 
 ```julia
 using Pkg
@@ -44,7 +43,7 @@ Loading and instantiating a gradient tree-boosting model:
 using MLJ
 Booster = @load EvoTreeRegressor # loads code defining a model type
 booster = Booster(max_depth=2)   # specify hyper-parameter at construction
-booster.nrounds=50               # or mutate afterwards
+booster.nrounds = 50             # or mutate afterwards
 ```
 
 This model is an example of an iterative model. As it stands, the
@@ -92,7 +91,7 @@ it "self-tuning":
 ```julia
 self_tuning_pipe = TunedModel(model=pipe,
                               tuning=RandomSearch(),
-                              ranges = max_depth_range,
+                              ranges=max_depth_range,
                               resampling=CV(nfolds=3, rng=456),
                               measure=l1,
                               acceleration=CPUThreads(),
@@ -105,7 +104,7 @@ Loading a selection of features and labels from the Ames
 House Price dataset:
 
 ```julia
-X, y = @load_reduced_ames;
+X, y = @load_reduced_ames
 ```
 Evaluating the "self-tuning" pipeline model's performance using 5-fold
 cross-validation (implies multiple layers of nested resampling):
@@ -155,8 +154,7 @@ Extract:
 
 * Consistent interface to handle probabilistic predictions.
 
-* Extensible [tuning
-  interface](https://github.com/JuliaAI/MLJTuning.jl),
+* Extensible [tuning interface](https://github.com/JuliaAI/MLJTuning.jl),
   to support a growing number of optimization strategies, and designed
   to play well with model composition.
 
diff --git a/docs/src/adding_models_for_general_use.md b/docs/src/adding_models_for_general_use.md
old mode 100755
new mode 100644
index a63a8ac3f..c51836c04
--- a/docs/src/adding_models_for_general_use.md
+++ b/docs/src/adding_models_for_general_use.md
@@ -5,4 +5,4 @@ suitable for addition to the MLJ Model Registry, consult the [MLJModelInterface.
 documentation](https://juliaai.github.io/MLJModelInterface.jl/dev/).
 
 For quick-and-dirty user-defined models see [Simple User Defined
-Models](simple_user_defined_models.md). 
+Models](simple_user_defined_models.md).
diff --git a/docs/src/api.md b/docs/src/api.md
old mode 100755
new mode 100644
diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index 72491a81f..a7fbc69af 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -55,7 +55,7 @@ Horizontally splitting data and shuffling rows.
 Here `y` is the `:Exit` column and `X` a table with everything else:
 
 ```@example workflows
-y, X = unpack(channing, ==(:Exit), rng=123);
+y, X = unpack(channing, ==(:Exit), rng=123)
 nothing # hide
 ```
 
@@ -202,7 +202,7 @@ Do `measures()` to list all losses and scores and their aliases, or refer to the
 StatisticalMeasures.jl [docs](https://juliaai.github.io/StatisticalMeasures.jl/dev/).
 
 
-##  Basic fit/evaluate/predict by hand:
+##  Basic fit/evaluate/predict by hand
 
 *Reference:*   [Getting Started](index.md), [Machines](machines.md),
 [Evaluating Model Performance](evaluating_model_performance.md), [Performance Measures](performance_measures.md)
@@ -496,7 +496,7 @@ Tree = @load DecisionTreeRegressor pkg=DecisionTree verbosity=0
 tree_with_target = TransformedTargetModel(model=Tree(),
                                           transformer=y -> log.(y),
                                           inverse = z -> exp.(z))
-pipe2 = (X -> coerce(X, :age=>Continuous)) |> OneHotEncoder() |> tree_with_target;
+pipe2 = (X -> coerce(X, :age=>Continuous)) |> OneHotEncoder() |> tree_with_target
 nothing # hide
 ```
 
diff --git a/docs/src/controlling_iterative_models.md b/docs/src/controlling_iterative_models.md
index e0cabdb40..bbe8ddaa0 100644
--- a/docs/src/controlling_iterative_models.md
+++ b/docs/src/controlling_iterative_models.md
@@ -98,7 +98,7 @@ control                                                        | description
 [`TimeLimit`](@ref EarlyStopping.TimeLimit)`(t=0.5)`           | Stop after `t` hours                                                                    | yes
 [`NumberLimit`](@ref EarlyStopping.NumberLimit)`(n=100)`       | Stop after `n` applications of the control                                              | yes
 [`NumberSinceBest`](@ref EarlyStopping.NumberSinceBest)`(n=6)` | Stop when best loss occurred `n` control applications ago                               | yes
-[`InvalidValue`](@ref IterationControl.InvalidValue)()         | Stop when `NaN`, `Inf` or `-Inf` loss/training loss encountered                         | yes 
+[`InvalidValue`](@ref IterationControl.InvalidValue)()         | Stop when `NaN`, `Inf` or `-Inf` loss/training loss encountered                         | yes
 [`Threshold`](@ref EarlyStopping.Threshold)`(value=0.0)`       | Stop when `loss < value`                                                                | yes
 [`GL`](@ref EarlyStopping.GL)`(alpha=2.0)`                     | † Stop after the "generalization loss (GL)" exceeds `alpha`                             | yes
 [`PQ`](@ref EarlyStopping.PQ)`(alpha=0.75, k=5)`               | † Stop after "progress-modified GL" exceeds `alpha`                                     | yes
@@ -109,15 +109,15 @@ control                                                        | description
 [`Error`](@ref IterationControl.Error)`(predicate; f="")`      | Log to `Error` the value of `f` or `f(mach)`, if `predicate(mach)` holds and then stop  | yes
 [`Callback`](@ref IterationControl.Callback)`(f=mach->nothing)`| Call `f(mach)`                                                                          | yes
 [`WithNumberDo`](@ref IterationControl.WithNumberDo)`(f=n->@info(n))`                       | Call `f(n + 1)` where `n` is the number of complete control cycles so far | yes
-[`WithIterationsDo`](@ref MLJIteration.WithIterationsDo)`(f=i->@info("iterations: $i"))`| Call `f(i)`, where `i` is total number of iterations           | yes
+[`WithIterationsDo`](@ref MLJIteration.WithIterationsDo)`(f=i->@info("iterations: $i"))`    | Call `f(i)`, where `i` is total number of iterations       | yes
 [`WithLossDo`](@ref IterationControl.WithLossDo)`(f=x->@info("loss: $x"))`                  | Call `f(loss)` where `loss` is the current loss            | yes
-[`WithTrainingLossesDo`](@ref IterationControl.WithTrainingLossesDo)`(f=v->@info(v))`       | Call `f(v)` where `v` is the current batch of training losses | yes
-[`WithEvaluationDo`](@ref MLJIteration.WithEvaluationDo)`(f->e->@info("evaluation: $e))`| Call `f(e)` where `e` is the current performance evaluation object | yes
+[`WithTrainingLossesDo`](@ref IterationControl.WithTrainingLossesDo)`(f=v->@info(v))`       | Call `f(v)` where `v` is the current batch of training losses         | yes
+[`WithEvaluationDo`](@ref MLJIteration.WithEvaluationDo)`(f->e->@info("evaluation: $e))`    | Call `f(e)` where `e` is the current performance evaluation object    | yes
 [`WithFittedParamsDo`](@ref MLJIteration.WithFittedParamsDo)`(f->fp->@info("fitted_params: $fp))`| Call `f(fp)` where `fp` is fitted parameters of training machine | yes
-[`WithReportDo`](@ref MLJIteration.WithReportDo)`(f->e->@info("report: $e))`| Call `f(r)` where `r` is the training machine report                    | yes
-[`WithModelDo`](@ref MLJIteration.WithModelDo)`(f->m->@info("model: $m))`| Call `f(m)` where `m` is the model, which may be mutated by `f`             | yes
-[`WithMachineDo`](@ref MLJIteration.WithMachineDo)`(f->mach->@info("report: $mach))`| Call `f(mach)` wher `mach` is the training machine in its current state    | yes
-[`Save`](@ref MLJIteration.Save)`(filename="machine.jls")`|Save current training machine to `machine1.jls`, `machine2.jsl`, etc                         | yes
+[`WithReportDo`](@ref MLJIteration.WithReportDo)`(f->e->@info("report: $e))`| Call `f(r)` where `r` is the training machine report                       | yes
+[`WithModelDo`](@ref MLJIteration.WithModelDo)`(f->m->@info("model: $m))`| Call `f(m)` where `m` is the model, which may be mutated by `f`               | yes
+[`WithMachineDo`](@ref MLJIteration.WithMachineDo)`(f->mach->@info("report: $mach))`| Call `f(mach)` wher `mach` is the training machine in its current state       | yes
+[`Save`](@ref MLJIteration.Save)`(filename="machine.jls")`     | Save current training machine to `machine1.jls`, `machine2.jsl`, etc                    | yes
 
 > Table 1. Atomic controls. Some advanced options are omitted.
 
diff --git a/docs/src/evaluating_model_performance.md b/docs/src/evaluating_model_performance.md
index c378b46ec..3999a1d97 100644
--- a/docs/src/evaluating_model_performance.md
+++ b/docs/src/evaluating_model_performance.md
@@ -96,7 +96,7 @@ evaluate!(
 )
 ```
 
-Or the user can define their own re-usable `ResamplingStrategy` objects, - see [Custom
+Or the user can define their own re-usable `ResamplingStrategy` objects; see [Custom
 resampling strategies](@ref) below.
 
 
@@ -170,4 +170,3 @@ function train_test_pairs(holdout::Holdout, rows)
     return [(train, test),]
 end
 ```
-
diff --git a/docs/src/frequently_asked_questions.md b/docs/src/frequently_asked_questions.md
old mode 100755
new mode 100644
diff --git a/docs/src/getting_started.md b/docs/src/getting_started.md
index 6ddf5f89f..5459597be 100644
--- a/docs/src/getting_started.md
+++ b/docs/src/getting_started.md
@@ -260,7 +260,7 @@ evaluate!(mach, resampling=Holdout(fraction_train=0.7),
 Changing a hyperparameter and re-evaluating:
 
 ```@repl doda
-tree.max_depth = 3
+tree.max_depth = 3;
 evaluate!(mach, resampling=Holdout(fraction_train=0.7),
           measures=[log_loss, accuracy],
           verbosity=0)
diff --git a/docs/src/glossary.md b/docs/src/glossary.md
index 94aa925e9..02a8a942d 100755
--- a/docs/src/glossary.md
+++ b/docs/src/glossary.md
@@ -93,8 +93,8 @@ wrapped in an associated operation (e.g., `predict` or
 `inverse_transform`). It consists primarily of:
 
 1. An operation, static or dynamic.
-1. A machine, or `nothing` if the operation is static.
-1. Upstream connections to other nodes, specified by a list of
+2. A machine, or `nothing` if the operation is static.
+3. Upstream connections to other nodes, specified by a list of
    *arguments* (one for each argument of the operation). These are the
    arguments on which the operation "acts" when the node `N` is
    called, as in `N()`.
diff --git a/docs/src/img/two_model_stack.png b/docs/src/img/two_model_stack.png
old mode 100755
new mode 100644
diff --git a/docs/src/img/wrapped_ridge.png b/docs/src/img/wrapped_ridge.png
old mode 100755
new mode 100644
diff --git a/docs/src/internals.md b/docs/src/internals.md
old mode 100755
new mode 100644
diff --git a/docs/src/learning_networks.md b/docs/src/learning_networks.md
index c275ba543..0eebc8a0c 100644
--- a/docs/src/learning_networks.md
+++ b/docs/src/learning_networks.md
@@ -224,7 +224,7 @@ A more complicated learning network may contain machines that can be trained in
 parallel. In that case, a call like the following may speed up training:
 
 ```@example 42
-tree.max_depth=2
+tree.max_depth = 2
 fit!(yhat, acceleration=CPUThreads())
 nothing # hide
 ```
diff --git a/docs/src/loading_model_code.md b/docs/src/loading_model_code.md
index 850e5a262..d0489ffa3 100644
--- a/docs/src/loading_model_code.md
+++ b/docs/src/loading_model_code.md
@@ -42,7 +42,7 @@ MLJDecisionTreeInterface.jl. If this package is not in `my_env` (do
 `Pkg.status()` to check) you add it by running
 
 ```julia-repl
-julia> Pkg.add("MLJDecisionTreeInterface");
+julia> Pkg.add("MLJDecisionTreeInterface")
 ```
 
 So long as `my_env` is the active environment, this action need never
diff --git a/docs/src/machines.md b/docs/src/machines.md
index 7ad56c935..b52579e9b 100644
--- a/docs/src/machines.md
+++ b/docs/src/machines.md
@@ -33,7 +33,7 @@ Generally, changing a hyperparameter triggers retraining on calls to
 subsequent `fit!`:
 
 ```@repl machines
-forest.bagging_fraction = 0.5
+forest.bagging_fraction = 0.5;
 fit!(mach, verbosity=2);
 ```
 
@@ -41,7 +41,7 @@ However, for this iterative model, increasing the iteration parameter
 only adds models to the existing ensemble:
 
 ```@repl machines
-forest.n = 15
+forest.n = 15;
 fit!(mach, verbosity=2);
 ```
 
@@ -138,8 +138,8 @@ such as a vector of per-observation weights (in which case
 --------------------|-----------------------------|--------------------------------------
 `Deterministic <: Supervised`    | `machine(model, X, y, extras...)` | `predict(mach, Xnew)`, `transform(mach, Xnew)`, `inverse_transform(mach, Xout)`
 `Probabilistic <: Supervised`    | `machine(model, X, y, extras...)` | `predict(mach, Xnew)`, `predict_mean(mach, Xnew)`, `predict_median(mach, Xnew)`, `predict_mode(mach, Xnew)`, `transform(mach, Xnew)`, `inverse_transform(mach, Xout)`
-`Unsupervised` (except `Static`) | `machine(model, X)` | `transform(mach, Xnew)`, `inverse_transform(mach, Xout)`, `predict(mach, Xnew)`
-`Static`                        | `machine(model)`    | `transform(mach, Xnews...)`, `inverse_transform(mach, Xout)`
+`Unsupervised` (except `Static`) | `machine(model, X)`               | `transform(mach, Xnew)`, `inverse_transform(mach, Xout)`, `predict(mach, Xnew)`
+`Static`                         | `machine(model)`                  | `transform(mach, Xnews...)`, `inverse_transform(mach, Xout)`
 
 All operations on machines (`predict`, `transform`, etc) have exactly
 one argument (`Xnew` or `Xout` above) after `mach`, the machine
diff --git a/docs/src/preparing_data.md b/docs/src/preparing_data.md
index 5b73dfa5f..763bb522e 100644
--- a/docs/src/preparing_data.md
+++ b/docs/src/preparing_data.md
@@ -40,7 +40,7 @@ models(matching(X, y))
 Or are unsure about the source of the following warning:
 
 ```julia-repl
-julia> Tree = @load DecisionTreeRegressor pkg=DecisionTree verbosity=0
+julia> Tree = @load DecisionTreeRegressor pkg=DecisionTree verbosity=0;
 julia> tree = Tree();
 
 julia> machine(tree, X, y)
@@ -57,7 +57,7 @@ Machine{DecisionTreeRegressor,…} @198 trained 0 times; caches data
 The meaning of the warning is:
 
 - The input `X` is a table with column scitypes `Continuous`, `Count`, and `Textual` and `Union{Missing, Textual}`, which can also see by inspecting the schema:
-	
+
 ```@example poot
 schema(X)
 ```
@@ -72,8 +72,7 @@ above, with links to further documentation given below:
 **Scientific type coercion:** We coerce machine types to obtain the
 intended scientific interpretation. If `height` in the above example
 is intended to be `Continuous`, `mark` is supposed to be
-`OrderedFactor`, and `admitted` a (binary) `Multiclass`, then we can
-do
+`OrderedFactor`, and `admitted` a (binary) `Multiclass`, then we can do
 
 ```@example poot
 X_coerced = coerce(X, :height=>Continuous, :mark=>OrderedFactor, :admitted=>Multiclass);
@@ -82,7 +81,7 @@ schema(X_coerced)
 
 **Data transformations:** We carry out conventional data
 transformations, such as missing value imputation and feature encoding:
-  
+
 ```@example poot
 imputer = FillImputer()
 mach = machine(imputer, X_coerced) |> fit!
diff --git a/docs/src/transformers.md b/docs/src/transformers.md
index 805e715d8..0c32ed2e6 100644
--- a/docs/src/transformers.md
+++ b/docs/src/transformers.md
@@ -201,7 +201,7 @@ using MLJ
 import Random.seed!
 seed!(123)
 
-X, y = @load_iris;
+X, y = @load_iris
 KMeans = @load KMeans pkg=ParallelKMeans
 kmeans = KMeans()
 mach = machine(kmeans, X) |> fit!
@@ -210,14 +210,14 @@ nothing # hide
 
 Transforming:
 ```@example predtrans
-Xsmall = transform(mach);
+Xsmall = transform(mach)
 selectrows(Xsmall, 1:4) |> pretty
 ```
 
 Predicting:
 ```@example predtrans
-yhat = predict(mach);
-compare = zip(yhat, y) |> collect;
+yhat = predict(mach)
+compare = zip(yhat, y) |> collect
 ```
 
 ```@example predtrans
diff --git a/docs/src/tuning_models.md b/docs/src/tuning_models.md
index 709547355..026f56112 100644
--- a/docs/src/tuning_models.md
+++ b/docs/src/tuning_models.md
@@ -121,7 +121,7 @@ Predicting on new input observations using the optimal model, *trained
 on all the data* bound to `mach`:
 
 ```@example goof
-Xnew  = MLJ.table(rand(3, 10));
+Xnew = MLJ.table(rand(3, 10));
 predict(mach, Xnew)
 ```
 
@@ -178,7 +178,7 @@ self_tuning_knn = TunedModel(
     resampling = CV(nfolds=4, rng=1234),
     tuning = Grid(resolution=5),
     range = K_range,
-    measure=BrierLoss()
+    measure = BrierLoss()
 );
 
 mach = machine(self_tuning_knn, X, y);
@@ -193,7 +193,7 @@ self_tuning_knn = TunedModel(
     resampling = CV(nfolds=4, rng=1234),
     tuning = Grid(resolution=5),
     range = K_range,
-    measure=MisclassificationRate()
+    measure = MisclassificationRate()
 )
 
 mach = machine(self_tuning_knn, X, y);

From c7b5d3ac85163c542ca2f6e8dbb1316fa6d0f186 Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 21:58:58 -0400
Subject: [PATCH 10/24] Fix indentation of markdown line

---
 docs/src/common_mlj_workflows.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index a7fbc69af..d60bc4096 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -165,7 +165,7 @@ nothing # hide
 
 ## Instantiating a model
 
-    *Reference:*   [Getting Started](@ref), [Loading Model Code](@ref)
+*Reference:*   [Getting Started](@ref), [Loading Model Code](@ref)
 
 Assumes `MLJDecisionTreeClassifier` is in your environment. Otherwise, try interactive
 loading with `@iload`:

From 925ec42d559209b57fbad3ca037b90624b275c7d Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:00:42 -0400
Subject: [PATCH 11/24] Move hidden example block to setup

---
 docs/src/common_mlj_workflows.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index d60bc4096..11ff279bf 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -373,8 +373,8 @@ z = transform(mach, y);
 
 *Reference:*   [Tuning Models](tuning_models.md)
 
-```@example workflows
-X, y = @load_iris; nothing # hide
+```@setup workflows
+X, y = @load_iris
 ```
 
 Define a model with nested hyperparameters:

From 2a1202f61bb61606054331a1e98a34813e22ffd7 Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:02:38 -0400
Subject: [PATCH 12/24] Pull code sample into list

---
 docs/src/preparing_data.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/src/preparing_data.md b/docs/src/preparing_data.md
index 763bb522e..a8c49cc4f 100644
--- a/docs/src/preparing_data.md
+++ b/docs/src/preparing_data.md
@@ -58,9 +58,9 @@ The meaning of the warning is:
 
 - The input `X` is a table with column scitypes `Continuous`, `Count`, and `Textual` and `Union{Missing, Textual}`, which can also see by inspecting the schema:
 
-```@example poot
-schema(X)
-```
+  ```@example poot
+  schema(X)
+  ```
 
 - The model requires a table whose column element scitypes subtype `Continuous`, an incompatibility.
 

From f8518f4536f163fe8f047308e38fed7ef833c971 Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:05:24 -0400
Subject: [PATCH 13/24] Use proper markdown lists

---
 docs/src/glossary.md       | 28 ++++++++++++++--------------
 docs/src/mlj_cheatsheet.md | 20 ++++++++++----------
 2 files changed, 24 insertions(+), 24 deletions(-)
 mode change 100755 => 100644 docs/src/glossary.md

diff --git a/docs/src/glossary.md b/docs/src/glossary.md
old mode 100755
new mode 100644
index 02a8a942d..425f45ec7
--- a/docs/src/glossary.md
+++ b/docs/src/glossary.md
@@ -47,20 +47,20 @@ on a fit-result (e.g., a broadcasted logarithm) which is then called
 
 An object consisting of:
 
-(1) A model
-
-(2) A fit-result (undefined until training)
-
-(3) *Training arguments* (one for each data argument of the model's
-associated `fit` method). A training argument is data used for
-training (subsampled by specifying `rows=...` in `fit!`) but also in
-evaluation (subsampled by specifying `rows=...` in `predict`,
-`predict_mean`, etc). Generally, there are two training arguments for
-supervised models, and just one for unsupervised models. Each argument
-is either a `Source` node, wrapping concrete data supplied to the
-`machine` constructor, or a `Node`, in the case of a learning network
-(see below). Both kinds of nodes can be *called* with an optional
-`rows=...` keyword argument to (lazily) return concrete data.
+1. A model
+
+2. A fit-result (undefined until training)
+
+3. *Training arguments* (one for each data argument of the model's
+   associated `fit` method). A training argument is data used for
+   training (subsampled by specifying `rows=...` in `fit!`) but also in
+   evaluation (subsampled by specifying `rows=...` in `predict`,
+   `predict_mean`, etc). Generally, there are two training arguments for
+   supervised models, and just one for unsupervised models. Each argument
+   is either a `Source` node, wrapping concrete data supplied to the
+   `machine` constructor, or a `Node`, in the case of a learning network
+   (see below). Both kinds of nodes can be *called* with an optional
+   `rows=...` keyword argument to (lazily) return concrete data.
 
 In addition, machines store "report" metadata, for recording
 algorithm-specific statistics of training (eg, an internal estimate of
diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index fec42f2a0..11873358b 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -66,11 +66,11 @@ type                                       | scitype
 
 Use `schema(X)` to get the column scitypes of a table `X`
 
-`coerce(y, Multiclass)` attempts coercion of all elements of `y` into scitype `Multiclass`
+- `coerce(y, Multiclass)` attempts coercion of all elements of `y` into scitype `Multiclass`
 
-`coerce(X, :x1 => Continuous, :x2 => OrderedFactor)` to coerce columns `:x1` and `:x2` of table `X`.
+- `coerce(X, :x1 => Continuous, :x2 => OrderedFactor)` to coerce columns `:x1` and `:x2` of table `X`.
 
-`coerce(X, Count => Continuous)` to coerce all columns with `Count` scitype to `Continuous`.
+- `coerce(X, Count => Continuous)` to coerce all columns with `Count` scitype to `Continuous`.
 
 
 ## Ingesting data
@@ -152,11 +152,11 @@ mach = machine(model, X)
 
 ## Prediction
 
-Supervised case: `predict(mach, Xnew)` or `predict(mach, rows=1:100)`
+- Supervised case: `predict(mach, Xnew)` or `predict(mach, rows=1:100)`
 
-Similarly, for probabilistic models: `predict_mode`, `predict_mean` and `predict_median`.
+  Similarly, for probabilistic models: `predict_mode`, `predict_mean` and `predict_median`.
 
-Unsupervised case: `transform(mach, rows=1:100)` or `inverse_transform(mach, rows)`, etc.
+- Unsupervised case: `transform(mach, rows=1:100)` or `inverse_transform(mach, rows)`, etc.
 
 
 ## Inspecting objects
@@ -302,13 +302,13 @@ Externals include: `PCA` (in MultivariateStats), `KMeans`, `KMedoids` (in Cluste
 
 `pipe = (X -> coerce(X, :height=>Continuous)) |> OneHotEncoder |> KNNRegressor(K=3)`
 
-Unsupervised:
+- Unsupervised:
 
-`pipe = Standardizer |> OneHotEncoder`
+  `pipe = Standardizer |> OneHotEncoder`
 
-Concatenation:
+- Concatenation:
 
-`pipe1 |> pipe2` or `model |> pipe` or `pipe |> model`, etc
+  `pipe1 |> pipe2` or `model |> pipe` or `pipe |> model`, etc.
 
 ## Advanced model composition techniques
 

From ad9129b8525392b94d8ba10820647e28eec07f6e Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:43:46 -0400
Subject: [PATCH 14/24] Use example block for workflows

---
 docs/src/common_mlj_workflows.md | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/docs/src/common_mlj_workflows.md b/docs/src/common_mlj_workflows.md
index 11ff279bf..dac37e18b 100644
--- a/docs/src/common_mlj_workflows.md
+++ b/docs/src/common_mlj_workflows.md
@@ -23,14 +23,16 @@ MLJ_VERSION
 ## Data ingestion
 
 ```@setup workflows
-# to avoid RDatasets as a doc dependency:
+# to avoid RDatasets as a doc dependency, generate synthetic data with
+# similar parameters, with the first four rows mimicking the original dataset
+# for display purposes
 color_off()
 import DataFrames
-channing = (Sex = rand(["Male","Female"], 462),
-            Entry = rand(Int, 462),
-            Exit = rand(Int, 462),
-            Time = rand(Int, 462),
-            Cens = rand(Int, 462)) |> DataFrames.DataFrame
+channing = (Sex = [repeat(["Male"], 4)..., rand(["Male","Female"], 458)...],
+            Entry = Int32[782, 1020, 856, 915, rand(733:1140, 458)...],
+            Exit = Int32[909, 1128, 969, 957, rand(777:1207, 458)...],
+            Time = Int32[127, 108, 113, 42, rand(0:137, 458)...],
+            Cens = Int32[1, 1, 1, 1, rand(0:1, 458)...]) |> DataFrames.DataFrame
 coerce!(channing, :Sex => Multiclass)
 ```
 
@@ -41,7 +43,7 @@ channing = RDatasets.dataset("boot", "channing")
 ```
 
 ```@example workflows
-first(channing, 4)
+first(channing, 4) |> pretty
 ```
 
 Inspecting metadata, including column scientific types:

From da2e45a6f69eefb920131d819c26942ecd904116 Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:44:07 -0400
Subject: [PATCH 15/24] Remove lambdas

---
 docs/src/learning_networks.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/src/learning_networks.md b/docs/src/learning_networks.md
index 0eebc8a0c..d4474aafa 100644
--- a/docs/src/learning_networks.md
+++ b/docs/src/learning_networks.md
@@ -125,7 +125,7 @@ broadcasted versions of `log`, `exp`, `mean`, `mode` and `median`. A function li
 is not overloaded, so that `Q = sqrt(Z)` will throw an error. Instead, we do
 
 ```@example 42
-Q = node(z->sqrt(z), Z)
+Q = node(sqrt, Z)
 Z()
 ```
 
@@ -736,7 +736,7 @@ There is also an experimental macro [`@node`](@ref). If `Z` is an `AbstractNode`
 source(16)`, say) then instead of
 
 ```julia
-Q = node(z->sqrt(z), Z)
+Q = node(sqrt, Z)
 ```
 
 one can do

From 72f2be25ac8eee80f65247c81b93c533f1470987 Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:45:10 -0400
Subject: [PATCH 16/24] Use repl blocks for user defined models

---
 docs/src/simple_user_defined_models.md | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)
 mode change 100755 => 100644 docs/src/simple_user_defined_models.md

diff --git a/docs/src/simple_user_defined_models.md b/docs/src/simple_user_defined_models.md
old mode 100755
new mode 100644
index ffc64920b..e3e779aed
--- a/docs/src/simple_user_defined_models.md
+++ b/docs/src/simple_user_defined_models.md
@@ -39,9 +39,9 @@ For an unsupervised model, implement `transform` and, optionally,
 Here's a quick-and-dirty implementation of a ridge regressor with no intercept:
 
 ```@example regressor_example
+using MLJ; color_off() # hide
 import MLJBase
 using LinearAlgebra
-MLJBase.color_off() # hide
 
 mutable struct MyRegressor <: MLJBase.Deterministic
     lambda::Float64
@@ -65,11 +65,11 @@ nothing # hide
 After loading this code, all MLJ's basic meta-algorithms can be applied to `MyRegressor`:
 
 ```@repl regressor_example
+using MLJ # hide
 X, y = @load_boston;
 model = MyRegressor(lambda=1.0)
 regressor = machine(model, X, y)
 evaluate!(regressor, resampling=CV(), measure=rms, verbosity=0)
-
 ```
 
 ## A simple probabilistic classifier
@@ -78,7 +78,8 @@ The following probabilistic model simply fits a probability
 distribution to the `MultiClass` training target (i.e., ignores `X`)
 and returns this pdf for any new pattern:
 
-```julia
+```@example classifier_example
+using MLJ # hide
 import MLJBase
 import Distributions
 
@@ -99,11 +100,8 @@ MLJBase.predict(model::MyClassifier, fitresult, Xnew) =
     [fitresult for r in 1:nrows(Xnew)]
 ```
 
-```julia-repl
-julia> X, y = @load_iris;
-julia> mach = fit!(machine(MyClassifier(), X, y));
-julia> predict(mach, selectrows(X, 1:2))
-2-element Array{UnivariateFinite{String,UInt32,Float64},1}:
- UnivariateFinite(setosa=>0.333, versicolor=>0.333, virginica=>0.333)
- UnivariateFinite(setosa=>0.333, versicolor=>0.333, virginica=>0.333)
+```@repl classifier_example
+X, y = @load_iris;
+mach = machine(MyClassifier(), X, y) |> fit!;
+predict(mach, selectrows(X, 1:2))
 ```

From c24a96b27cfec242c870b69ae86af85957230d3d Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:50:52 -0400
Subject: [PATCH 17/24] Use bigger fences for cheatsheet code

---
 docs/src/mlj_cheatsheet.md | 63 +++++++++++++++++++++++++++-----------
 1 file changed, 45 insertions(+), 18 deletions(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 11873358b..ac58eb6a1 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -38,7 +38,10 @@ models() do model
 end
 ```
 
-`Tree = @load DecisionTreeClassifier pkg=DecisionTree` imports "DecisionTreeClassifier" type and binds it to `Tree`.
+```julia
+Tree = @load DecisionTreeClassifier pkg=DecisionTree
+```
+imports "DecisionTreeClassifier" type and binds it to `Tree`.
 
 `tree = Tree()` to instantiate a `Tree`.
 
@@ -90,8 +93,8 @@ Same as above but exclude `:Time` column from `X`:
 using RDatasets
 channing = dataset("boot", "channing")
 y, X =  unpack(channing,
-               ==(:Exit),            # y is the :Exit column
-               !=(:Time);            # X is the rest, except :Time
+               ==(:Exit),     # y is the :Exit column
+               !=(:Time);     # X is the rest, except :Time
                rng=123)
 ```
 
@@ -117,8 +120,8 @@ Getting data from [OpenML](https://www.openml.org):
 ```julia
 table = OpenML.load(91)
 ```
-Creating synthetic classification data:
 
+Creating synthetic classification data:
 ```julia
 X, y = make_blobs(100, 2)
 ```
@@ -147,8 +150,10 @@ mach = machine(model, X)
 
 ## Fitting
 
-`fit!(mach, rows=1:100, verbosity=1, force=false)` (defaults shown)
-
+The `fit!` function can be used to fit a machine (defaults shown):
+```julia
+fit!(mach, rows=1:100, verbosity=1, force=false)
+```
 
 ## Prediction
 
@@ -188,11 +193,17 @@ pkg="MultivariateStats")` gets all properties (aka traits) of registered models
 
 ## Performance estimation
 
-`evaluate(model, X, y, resampling=CV(), measure=rms, operation=predict, weights=..., verbosity=1)`
+```julia
+evaluate(model, X, y, resampling=CV(), measure=rms, operation=predict, weights=..., verbosity=1)
+```
 
-`evaluate!(mach, resampling=Holdout(), measure=[rms, mav], operation=predict, weights=..., verbosity=1)`
+```julia
+evaluate!(mach, resampling=Holdout(), measure=[rms, mav], operation=predict, weights=..., verbosity=1)
+```
 
-`evaluate!(mach, resampling=[(fold1, fold2), (fold2, fold1)], measure=rms)`
+```julia
+evaluate!(mach, resampling=[(fold1, fold2), (fold2, fold1)], measure=rms)
+```
 
 ## Resampling strategies (`resampling=...`)
 
@@ -212,7 +223,9 @@ or a list of pairs of row indices:
 
 ### Tuning model wrapper
 
-`tuned_model = TunedModel(model=…, tuning=RandomSearch(), resampling=Holdout(), measure=…, operation=predict, range=…)`
+```julia
+tuned_model = TunedModel(model=…, tuning=RandomSearch(), resampling=Holdout(), measure=…, operation=predict, range=…)
+```
 
 ### Ranges for tuning (`range=...`)
 
@@ -242,20 +255,28 @@ Also available: `LatinHyperCube`, `Explicit` (built-in), `MLJTreeParzenTuning`,
 
 For generating a plot of performance against parameter specified by `range`:
 
-`curve = learning_curve(mach, resolution=30, resampling=Holdout(), measure=…, operation=predict, range=…, n=1)`
+```julia
+curve = learning_curve(mach, resolution=30, resampling=Holdout(), measure=…, operation=predict, range=…, n=1)
+```
 
-`curve = learning_curve(model, X, y, resolution=30, resampling=Holdout(), measure=…, operation=predict, range=…, n=1)`
+```julia
+curve = learning_curve(model, X, y, resolution=30, resampling=Holdout(), measure=…, operation=predict, range=…, n=1)
+```
 
 If using Plots.jl:
 
-`plot(curve.parameter_values, curve.measurements, xlab=curve.parameter_name, xscale=curve.parameter_scale)`
+```julia
+plot(curve.parameter_values, curve.measurements, xlab=curve.parameter_name, xscale=curve.parameter_scale)
+```
 
 
 ## Controlling iterative models
 
 Requires: `using MLJIteration`
 
-`iterated_model = IteratedModel(model=…, resampling=Holdout(), measure=…, controls=…, retrain=false)`
+```julia
+iterated_model = IteratedModel(model=…, resampling=Holdout(), measure=…, controls=…, retrain=false)
+```
 
 
 ### Controls
@@ -291,16 +312,22 @@ Externals include: `PCA` (in MultivariateStats), `KMeans`, `KMedoids` (in Cluste
 
 ## Ensemble model wrapper
 
-`EnsembleModel(atom=…, weights=Float64[], bagging_fraction=0.8, rng=GLOBAL_RNG, n=100, parallel=true, out_of_bag_measure=[])`
+```julia
+EnsembleModel(atom=…, weights=Float64[], bagging_fraction=0.8, rng=GLOBAL_RNG, n=100, parallel=true, out_of_bag_measure=[])
+```
 
 
 ## Target transformation wrapper
 
-`TransformedTargetModel(model=ConstantClassifier(), target=Standardizer())`
+```julia
+TransformedTargetModel(model=ConstantClassifier(), target=Standardizer())
+```
 
 ## Pipelines
 
-`pipe = (X -> coerce(X, :height=>Continuous)) |> OneHotEncoder |> KNNRegressor(K=3)`
+```julia
+pipe = (X -> coerce(X, :height=>Continuous)) |> OneHotEncoder |> KNNRegressor(K=3)
+```
 
 - Unsupervised:
 
@@ -312,4 +339,4 @@ Externals include: `PCA` (in MultivariateStats), `KMeans`, `KMedoids` (in Cluste
 
 ## Advanced model composition techniques
 
-See the [Composing Models](@ref) section of the MLJ manual. 
+See the [Composing Models](@ref) section of the MLJ manual.

From 331bac89c1663208b92a6630162f6216553c7df9 Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 22:54:12 -0400
Subject: [PATCH 18/24] Promote headers in cheatsheet

---
 docs/src/mlj_cheatsheet.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index ac58eb6a1..57cb24c46 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -219,15 +219,15 @@ or a list of pairs of row indices:
 
 `[(train1, eval1), (train2, eval2), ... (traink, evalk)]`
 
-## Tuning
 
-### Tuning model wrapper
+
+## Tuning model wrapper
 
 ```julia
 tuned_model = TunedModel(model=…, tuning=RandomSearch(), resampling=Holdout(), measure=…, operation=predict, range=…)
 ```
 
-### Ranges for tuning (`range=...`)
+## Ranges for tuning (`range=...`)
 
 If `r = range(KNNRegressor(), :K, lower=1, upper = 20, scale=:log)`
 
@@ -242,7 +242,7 @@ Nested ranges: Use dot syntax, as in `r = range(EnsembleModel(atom=tree), :(atom
 Can specify multiple ranges, as in `range=[r1, r2, r3]`. For more range options do `?Grid` or `?RandomSearch`
 
 
-### Tuning strategies
+## Tuning strategies
 
 `RandomSearch(rng=1234)` for basic random search
 
@@ -251,7 +251,7 @@ Can specify multiple ranges, as in `range=[r1, r2, r3]`. For more range options
 Also available: `LatinHyperCube`, `Explicit` (built-in), `MLJTreeParzenTuning`, `ParticleSwarm`, `AdaptiveParticleSwarm` (3rd-party packages)
 
 
-#### Learning curves
+### Learning curves
 
 For generating a plot of performance against parameter specified by `range`:
 

From 0acc87642bb5d9befe217046a87a0404861c167f Mon Sep 17 00:00:00 2001
From: Abhro R <5664668+abhro@users.noreply.github.com>
Date: Tue, 14 May 2024 23:10:15 -0400
Subject: [PATCH 19/24] Use Clustering.jl instead of ParallelKMeans

---
 docs/Project.toml        | 1 -
 docs/src/transformers.md | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/Project.toml b/docs/Project.toml
index d86571e62..b9718b4ea 100755
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -15,7 +15,6 @@ MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
 NearestNeighborModels = "636a865e-7cf4-491e-846c-de09b730eb36"
-ParallelKMeans = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ScientificTypesBase = "30f210dd-8aff-4c5f-94ba-8e64358c1161"
 StatisticalMeasures = "a19d573c-0a75-4610-95b3-7071388c7541"
diff --git a/docs/src/transformers.md b/docs/src/transformers.md
index 0c32ed2e6..59373b9eb 100644
--- a/docs/src/transformers.md
+++ b/docs/src/transformers.md
@@ -202,7 +202,7 @@ import Random.seed!
 seed!(123)
 
 X, y = @load_iris
-KMeans = @load KMeans pkg=ParallelKMeans
+KMeans = @load KMeans pkg=Clustering
 kmeans = KMeans()
 mach = machine(kmeans, X) |> fit!
 nothing # hide

From 18e9c9ff1208623de170c6302c033ae45c551bc4 Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Wed, 15 May 2024 09:57:57 -0400
Subject: [PATCH 20/24] Remove unsupported use of info() from cheatsheet

---
 docs/src/mlj_cheatsheet.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 57cb24c46..4a8edc82a 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -173,8 +173,6 @@ fit!(mach, rows=1:100, verbosity=1, force=false)
 `info(ConstantRegressor())`, `info("PCA")`, `info("RidgeRegressor",
 pkg="MultivariateStats")` gets all properties (aka traits) of registered models
 
-`info(rms)` gets all properties of a performance measure
-
 `schema(X)` get column names, types and scitypes, and nrows, of a table `X`
 
 `scitype(X)` gets the scientific type of `X`

From 739ca21661f9ee6fde6330c6fbb1dffca8995bcf Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Wed, 15 May 2024 16:37:38 -0400
Subject: [PATCH 21/24] Remove comments to have not as wide code lines

---
 docs/src/mlj_cheatsheet.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 4a8edc82a..5cd24bea5 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -92,11 +92,12 @@ Same as above but exclude `:Time` column from `X`:
 ```julia
 using RDatasets
 channing = dataset("boot", "channing")
-y, X =  unpack(channing,
-               ==(:Exit),     # y is the :Exit column
-               !=(:Time);     # X is the rest, except :Time
-               rng=123)
+y, X = unpack(channing,
+              ==(:Exit),
+              !=(:Time);
+              rng=123)
 ```
+Here, `y` is assigned the `:Exit` column, and `X` is assigned the rest, except `:Time`.
 
 Splitting row indices into train/validation/test, with seeded shuffling:
 

From f2113224e49551792394af6f27f1e92a247c2154 Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Wed, 15 May 2024 16:39:46 -0400
Subject: [PATCH 22/24] Add description of data coercion in cheatsheet

---
 docs/src/mlj_cheatsheet.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 5cd24bea5..6f4c7dd84 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -69,6 +69,8 @@ type                                       | scitype
 
 Use `schema(X)` to get the column scitypes of a table `X`
 
+To coerce the data into different scitypes, use the `coerce` function:
+
 - `coerce(y, Multiclass)` attempts coercion of all elements of `y` into scitype `Multiclass`
 
 - `coerce(X, :x1 => Continuous, :x2 => OrderedFactor)` to coerce columns `:x1` and `:x2` of table `X`.

From d0796444f1941d3b468428198b554734db632e23 Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Wed, 15 May 2024 16:44:37 -0400
Subject: [PATCH 23/24] Update docs/src/mlj_cheatsheet.md

Co-authored-by: Essam <essamwisam@outlook.com>
---
 docs/src/mlj_cheatsheet.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index 6f4c7dd84..c2dd70e0b 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -228,7 +228,7 @@ or a list of pairs of row indices:
 tuned_model = TunedModel(model=…, tuning=RandomSearch(), resampling=Holdout(), measure=…, operation=predict, range=…)
 ```
 
-## Ranges for tuning (`range=...`)
+## Ranges for tuning `(range=...)`
 
 If `r = range(KNNRegressor(), :K, lower=1, upper = 20, scale=:log)`
 

From 650ebbd6dd3fc33a041a18451f9320a46ddd4b8f Mon Sep 17 00:00:00 2001
From: abhro <5664668+abhro@users.noreply.github.com>
Date: Wed, 15 May 2024 22:35:02 -0400
Subject: [PATCH 24/24] Remove other occurence of `info` on measure

---
 docs/src/mlj_cheatsheet.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/src/mlj_cheatsheet.md b/docs/src/mlj_cheatsheet.md
index c2dd70e0b..ea8d0d032 100644
--- a/docs/src/mlj_cheatsheet.md
+++ b/docs/src/mlj_cheatsheet.md
@@ -299,7 +299,7 @@ Wraps: `MLJIteration.skip(control, predicate=1)`, `IterationControl.with_state_d
 
 Do `measures()` to get full list.
 
-`info(rms)` to list properties (aka traits) of the `rms` measure
+`?rms` in the REPL can provide information about the `rms` measure, and can be used with any measure or their aliases.
 
 
 ## Transformers