diff --git a/codigo/decision-tree.ipynb b/codigo/decision-tree.ipynb
index b390c3e..267c6f5 100644
--- a/codigo/decision-tree.ipynb
+++ b/codigo/decision-tree.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 595,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 596,
    "metadata": {},
    "outputs": [
     {
@@ -378,7 +378,7 @@
        "[992 rows x 50 columns]"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 596,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 597,
    "metadata": {},
    "outputs": [
     {
@@ -750,7 +750,7 @@
        "[295 rows x 50 columns]"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 597,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -762,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 598,
    "metadata": {},
    "outputs": [
     {
@@ -1122,7 +1122,7 @@
        "[295 rows x 49 columns]"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 598,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 599,
    "metadata": {},
    "outputs": [
     {
@@ -1498,7 +1498,7 @@
        "[147 rows x 50 columns]"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 599,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1510,7 +1510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 600,
    "metadata": {},
    "outputs": [
     {
@@ -1870,7 +1870,7 @@
        "[147 rows x 49 columns]"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 600,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1886,7 +1886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 601,
    "metadata": {},
    "outputs": [
     {
@@ -1992,7 +1992,7 @@
        "[147 rows x 2 columns]"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 601,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2004,7 +2004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 602,
    "metadata": {},
    "outputs": [
     {
@@ -2364,7 +2364,7 @@
        "[147 rows x 51 columns]"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 602,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2376,7 +2376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 603,
    "metadata": {},
    "outputs": [
     {
@@ -2736,7 +2736,7 @@
        "[147 rows x 49 columns]"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 603,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2753,7 +2753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 604,
    "metadata": {},
    "outputs": [
     {
@@ -3113,7 +3113,7 @@
        "[147 rows x 50 columns]"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 604,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3127,7 +3127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 605,
    "metadata": {},
    "outputs": [
     {
@@ -3245,7 +3245,7 @@
        "      <td>67</td>\n",
        "      <td>31</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -3341,7 +3341,7 @@
        "      <td>76</td>\n",
        "      <td>1</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>282</th>\n",
@@ -3474,11 +3474,11 @@
        "     ahiTotal.post  cesdTotal.post  ahiChange  ahiResponse  \n",
        "1               73               6         10         True  \n",
        "2               89              10         16         True  \n",
-       "4               67              31          7        False  \n",
+       "4               67              31          7         True  \n",
        "5               41              32          0        False  \n",
        "8               45              45        -14        False  \n",
        "..             ...             ...        ...          ...  \n",
-       "281             76               1          7        False  \n",
+       "281             76               1          7         True  \n",
        "282             63              17         -3        False  \n",
        "283             85              12         -6        False  \n",
        "287             76               9         12         True  \n",
@@ -3487,21 +3487,21 @@
        "[147 rows x 51 columns]"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 605,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "measurements_pre_post_intervention['ahiResponse'] = \\\n",
-    "    measurements_pre_post_intervention['ahiChange'] >= 10\n",
+    "    measurements_pre_post_intervention['ahiChange'] >= 5\n",
     "\n",
     "measurements_pre_post_intervention"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 606,
    "metadata": {},
    "outputs": [
     {
@@ -3618,7 +3618,7 @@
        "      <td>67</td>\n",
        "      <td>31</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3714,7 +3714,7 @@
        "      <td>76</td>\n",
        "      <td>1</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>-3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -3848,11 +3848,11 @@
        "     ahiTotal.post  cesdTotal.post  ahiChange  ahiResponse  cesdChange  \n",
        "1               73               6         10         True          -8  \n",
        "2               89              10         16         True           3  \n",
-       "4               67              31          7        False           0  \n",
+       "4               67              31          7         True           0  \n",
        "5               41              32          0        False           5  \n",
        "8               45              45        -14        False          15  \n",
        "..             ...             ...        ...          ...         ...  \n",
-       "281             76               1          7        False          -3  \n",
+       "281             76               1          7         True          -3  \n",
        "282             63              17         -3        False           6  \n",
        "283             85              12         -6        False           4  \n",
        "287             76               9         12         True         -21  \n",
@@ -3861,7 +3861,7 @@
        "[147 rows x 52 columns]"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 606,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3875,7 +3875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 607,
    "metadata": {},
    "outputs": [
     {
@@ -3945,7 +3945,7 @@
        "      <td>10</td>\n",
        "      <td>True</td>\n",
        "      <td>-8</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -3991,7 +3991,7 @@
        "      <td>67</td>\n",
        "      <td>31</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
@@ -4087,7 +4087,7 @@
        "      <td>76</td>\n",
        "      <td>1</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>-3</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
@@ -4220,13 +4220,13 @@
        "292      4  ...       1       4            50             30             70   \n",
        "\n",
        "     cesdTotal.post  ahiChange  ahiResponse  cesdChange  cesdResponse  \n",
-       "1                 6         10         True          -8         False  \n",
+       "1                 6         10         True          -8          True  \n",
        "2                10         16         True           3         False  \n",
-       "4                31          7        False           0         False  \n",
+       "4                31          7         True           0         False  \n",
        "5                32          0        False           5         False  \n",
        "8                45        -14        False          15         False  \n",
        "..              ...        ...          ...         ...           ...  \n",
-       "281               1          7        False          -3         False  \n",
+       "281               1          7         True          -3         False  \n",
        "282              17         -3        False           6         False  \n",
        "283              12         -6        False           4         False  \n",
        "287               9         12         True         -21          True  \n",
@@ -4235,21 +4235,21 @@
        "[147 rows x 53 columns]"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 607,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "measurements_pre_post_intervention['cesdResponse'] = \\\n",
-    "    measurements_pre_post_intervention['cesdChange'] <= -10\n",
+    "    measurements_pre_post_intervention['cesdChange'] <= -5\n",
     "\n",
     "measurements_pre_post_intervention"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 608,
    "metadata": {},
    "outputs": [
     {
@@ -4403,7 +4403,7 @@
        "[295 rows x 6 columns]"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 608,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4415,7 +4415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 609,
    "metadata": {},
    "outputs": [
     {
@@ -4557,7 +4557,7 @@
        "[295 rows x 5 columns]"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 609,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4573,7 +4573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 610,
    "metadata": {},
    "outputs": [
     {
@@ -4703,7 +4703,7 @@
        "[295 rows x 4 columns]"
       ]
      },
-     "execution_count": 239,
+     "execution_count": 610,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4715,7 +4715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 611,
    "metadata": {},
    "outputs": [
     {
@@ -4785,7 +4785,7 @@
        "      <td>10</td>\n",
        "      <td>True</td>\n",
        "      <td>-8</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -4831,7 +4831,7 @@
        "      <td>67</td>\n",
        "      <td>31</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
@@ -4927,7 +4927,7 @@
        "      <td>76</td>\n",
        "      <td>1</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>-3</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
@@ -5060,13 +5060,13 @@
        "292  ...       1       4            50             30             70   \n",
        "\n",
        "     cesdTotal.post  ahiChange  ahiResponse  cesdChange  cesdResponse  \n",
-       "1                 6         10         True          -8         False  \n",
+       "1                 6         10         True          -8          True  \n",
        "2                10         16         True           3         False  \n",
-       "4                31          7        False           0         False  \n",
+       "4                31          7         True           0         False  \n",
        "5                32          0        False           5         False  \n",
        "8                45        -14        False          15         False  \n",
        "..              ...        ...          ...         ...           ...  \n",
-       "281               1          7        False          -3         False  \n",
+       "281               1          7         True          -3         False  \n",
        "282              17         -3        False           6         False  \n",
        "283              12         -6        False           4         False  \n",
        "287               9         12         True         -21          True  \n",
@@ -5075,7 +5075,7 @@
        "[147 rows x 57 columns]"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 611,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5087,7 +5087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 612,
    "metadata": {},
    "outputs": [
     {
@@ -5179,7 +5179,7 @@
        "      <td>67</td>\n",
        "      <td>31</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
@@ -5323,7 +5323,7 @@
        "      <td>68</td>\n",
        "      <td>8</td>\n",
        "      <td>9</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>-16</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
@@ -5433,13 +5433,13 @@
        "\n",
        "     cesdTotal.post  ahiChange  ahiResponse  cesdChange  cesdResponse  \n",
        "2                10         16         True           3         False  \n",
-       "4                31          7        False           0         False  \n",
+       "4                31          7         True           0         False  \n",
        "5                32          0        False           5         False  \n",
        "8                45        -14        False          15         False  \n",
        "11               19         -3        False           0         False  \n",
        "..              ...        ...          ...         ...           ...  \n",
        "273               0          4        False          -1         False  \n",
-       "276               8          9        False         -16          True  \n",
+       "276               8          9         True         -16          True  \n",
        "282              17         -3        False           6         False  \n",
        "283              12         -6        False           4         False  \n",
        "287               9         12         True         -21          True  \n",
@@ -5447,7 +5447,7 @@
        "[102 rows x 57 columns]"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 612,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5459,7 +5459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 613,
    "metadata": {},
    "outputs": [
     {
@@ -5551,7 +5551,7 @@
        "      <td>67</td>\n",
        "      <td>31</td>\n",
        "      <td>7</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
@@ -5695,7 +5695,7 @@
        "      <td>68</td>\n",
        "      <td>8</td>\n",
        "      <td>9</td>\n",
-       "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>-16</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
@@ -5805,13 +5805,13 @@
        "\n",
        "     cesdTotal.post  ahiChange  ahiResponse  cesdChange  cesdResponse  \n",
        "2                10         16         True           3         False  \n",
-       "4                31          7        False           0         False  \n",
+       "4                31          7         True           0         False  \n",
        "5                32          0        False           5         False  \n",
        "8                45        -14        False          15         False  \n",
        "11               19         -3        False           0         False  \n",
        "..              ...        ...          ...         ...           ...  \n",
        "273               0          4        False          -1         False  \n",
-       "276               8          9        False         -16          True  \n",
+       "276               8          9         True         -16          True  \n",
        "282              17         -3        False           6         False  \n",
        "283              12         -6        False           4         False  \n",
        "287               9         12         True         -21          True  \n",
@@ -5819,7 +5819,7 @@
        "[102 rows x 56 columns]"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 613,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5838,7 +5838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 614,
    "metadata": {},
    "outputs": [
     {
@@ -6198,7 +6198,7 @@
        "[102 rows x 50 columns]"
       ]
      },
-     "execution_count": 243,
+     "execution_count": 614,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6218,39 +6218,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 615,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2       True\n",
+       "2      False\n",
        "4      False\n",
        "5      False\n",
        "8      False\n",
        "11     False\n",
        "       ...  \n",
        "273    False\n",
-       "276    False\n",
+       "276     True\n",
        "282    False\n",
        "283    False\n",
        "287     True\n",
-       "Name: ahiResponse, Length: 102, dtype: bool"
+       "Name: cesdResponse, Length: 102, dtype: bool"
       ]
      },
-     "execution_count": 244,
+     "execution_count": 615,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "outcome_variable = data['ahiResponse']\n",
+    "outcome_variable = data['cesdResponse']\n",
     "outcome_variable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
+   "execution_count": 616,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 617,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(1234)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 618,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6259,7 +6277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 619,
    "metadata": {},
    "outputs": [
     {
@@ -6291,7 +6309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 620,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6300,13 +6318,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 621,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-19 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -6336,15 +6354,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-19 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-19 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-19 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -6356,7 +6374,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-19 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -6364,7 +6382,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-19 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -6374,7 +6392,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-19 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -6390,14 +6408,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-19 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
+       "#sk-container-id-19 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -6405,28 +6423,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-19 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-19 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-19 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-19 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-19 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -6444,14 +6462,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-19 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-19 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -6461,7 +6479,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -6469,13 +6487,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "#sk-container-id-19 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -6484,12 +6502,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-19 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -6497,79 +6515,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-19 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-19 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-19 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-19 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-19 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-19 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-19 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
+       "#sk-container-id-19 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-19 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -6579,18 +6597,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-19 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-19 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-19 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -6677,7 +6695,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-19 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -6692,37 +6710,37 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-19 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-19 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=5)</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
-       "DecisionTreeClassifier()"
+       "DecisionTreeClassifier(criterion='entropy', max_depth=5)"
       ]
      },
-     "execution_count": 248,
+     "execution_count": 621,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model = DecisionTreeClassifier()\n",
+    "model = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
     "\n",
     "model.fit(train_x, train_y)"
    ]
@@ -6736,7 +6754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": 622,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6745,16 +6763,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 250,
+   "execution_count": 623,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6666666666666666"
+       "0.7142857142857143"
       ]
      },
-     "execution_count": 250,
+     "execution_count": 623,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6764,6 +6782,78 @@
     "\n",
     "accuracy_score(test_y, predicted_y)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 633,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0, 'Valor predito'), Text(0, 0.5, 'Valor real')]"
+      ]
+     },
+     "execution_count": 633,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "\n",
+    "confusion_matrix_display = ConfusionMatrixDisplay.from_predictions(\n",
+    "    test_y,\n",
+    "    predicted_y,\n",
+    "    cmap='Blues',\n",
+    "    colorbar=False,\n",
+    "    display_labels=['NR', 'R'])\n",
+    "\n",
+    "confusion_matrix_display.ax_.set(xlabel='Valor predito', ylabel='Valor real')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 628,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.tree import plot_tree\n",
+    "\n",
+    "plt.figure(figsize=(18, 18))\n",
+    "\n",
+    "plot_tree(model,\n",
+    "          feature_names=model.feature_names_in_,\n",
+    "          class_names=['NR', 'R'],\n",
+    "          fontsize=10,\n",
+    "          filled=True,\n",
+    "          rounded=True)\n",
+    "\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/codigo/requirements.txt b/codigo/requirements.txt
index 7d01ef7..3cabe44 100644
--- a/codigo/requirements.txt
+++ b/codigo/requirements.txt
@@ -4,4 +4,6 @@ pandas
 numpy
 scipy
 
-scikit-learn
\ No newline at end of file
+scikit-learn
+
+matplotlib
\ No newline at end of file
diff --git a/texto/03-exemplo/imagens/arvore.png b/texto/03-exemplo/imagens/arvore.png
new file mode 100644
index 0000000..43ca00b
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diff --git a/texto/03-exemplo/imagens/matriz.png b/texto/03-exemplo/imagens/matriz.png
new file mode 100644
index 0000000..6138292
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