Modelos Categóricos Ordinales

Modelos Categóricos Ordinales.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Modelos de Clasificación no Binarios\n---\n\n## Resumen\n\n\nEste experimento pretende probar varias formas de modelar una predicción categórica, bien sea ordinal o no, a través de distintas técnicas como ordenar, balancear, transformar los valores categóricos a numéricos para hacer una regresión. \n\nPara ello, he escogido el dataset de Titanic, en el que tenemos quién sobrevivió y quién no, la edad, género, edad, cuánto pagó, cuántos familiares viajaron con cada pasajero, clase de pasaje. Tengamos en cuenta que este dataset no es muy grande por lo que un par de casos clasificados bien o mal pueden variar notablemente algunos resultados.\n\nPara nuestro experimento, queremos predecir **a qué clase pertenecía cada pasajero** (1era, 2da ó 3ra). Como se darán cuenta, estos valores para `Pclass` parecen numéricos pero realmente deberían ser categóricos, ordinales. **Categóricos** porque tenemos apenas 3 valores posibles en todos los pasajeros; fíjate que podrían haber sido perfectamente clases A, B y C. **Ordinales** porque hay un orden lógico, creciente o decreciente: no es lo mismo confundir 1era clase con 3era clase que 2da con 3ra; hay una referencia importante de posición o aproximación que un valor categórico normal no tiene. \n\n> ¿Cómo podemos ayudar al modelo a entender las relaciones entre variables categóricas ordinales?"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "options(warn = -1)\nsuppressMessages(library(dplyr))\nsuppressMessages(library(ggplot2))\nsuppressMessages(library(lares))\nsuppressMessages(library(DMwR)) # SMOTE",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## DATOS\n\nCargamos los datos de Titanic. Este comando importará los datos y creará un `data.frame` llamado `dft`."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data(dft)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**ESTRUCTURA**: Chequeamos rápidamente en qué consisten los datos, cuántas columnas y filas tenemos, frecuencias, etc."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Cómo está compuesto dft?\ndf_str(dft, quiet = TRUE)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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p3+0qpFM4aI9nxq+cmO6j5TXvB4NCyAv352PVO0Gsvy5cuNHUItGEwDYWAabil2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\nAIAlEOwAAAAAWOL/Am+iI9nHcuX5AAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Veamos las primeras 10 filas\nhead(dft, 10)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A data.frame: 10 × 11</caption>\n<thead>\n\t<tr><th scope=col>PassengerId</th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Ticket</th><th scope=col>Fare</th><th scope=col>Cabin</th><th scope=col>Embarked</th></tr>\n\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td> 1</td><td>FALSE</td><td>3</td><td>male  </td><td>22</td><td>1</td><td>0</td><td>A/5 21171       </td><td> 7.2500</td><td>    </td><td>S</td></tr>\n\t<tr><td> 2</td><td> TRUE</td><td>1</td><td>female</td><td>38</td><td>1</td><td>0</td><td>PC 17599        </td><td>71.2833</td><td>C85 </td><td>C</td></tr>\n\t<tr><td> 3</td><td> TRUE</td><td>3</td><td>female</td><td>26</td><td>0</td><td>0</td><td>STON/O2. 3101282</td><td> 7.9250</td><td>    </td><td>S</td></tr>\n\t<tr><td> 4</td><td> TRUE</td><td>1</td><td>female</td><td>35</td><td>1</td><td>0</td><td>113803          </td><td>53.1000</td><td>C123</td><td>S</td></tr>\n\t<tr><td> 5</td><td>FALSE</td><td>3</td><td>male  </td><td>35</td><td>0</td><td>0</td><td>373450          </td><td> 8.0500</td><td>    </td><td>S</td></tr>\n\t<tr><td> 6</td><td>FALSE</td><td>3</td><td>male  </td><td>NA</td><td>0</td><td>0</td><td>330877          </td><td> 8.4583</td><td>    </td><td>Q</td></tr>\n\t<tr><td> 7</td><td>FALSE</td><td>1</td><td>male  </td><td>54</td><td>0</td><td>0</td><td>17463           </td><td>51.8625</td><td>E46 </td><td>S</td></tr>\n\t<tr><td> 8</td><td>FALSE</td><td>3</td><td>male  </td><td> 2</td><td>3</td><td>1</td><td>349909          </td><td>21.0750</td><td>    </td><td>S</td></tr>\n\t<tr><td> 9</td><td> TRUE</td><td>3</td><td>female</td><td>27</td><td>0</td><td>2</td><td>347742          </td><td>11.1333</td><td>    </td><td>S</td></tr>\n\t<tr><td>10</td><td> TRUE</td><td>2</td><td>female</td><td>14</td><td>1</td><td>0</td><td>237736          </td><td>30.0708</td><td>    </td><td>C</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA data.frame: 10 × 11\n\n| PassengerId &lt;int&gt; | Survived &lt;lgl&gt; | Pclass &lt;fct&gt; | Sex &lt;fct&gt; | Age &lt;dbl&gt; | SibSp &lt;int&gt; | Parch &lt;int&gt; | Ticket &lt;chr&gt; | Fare &lt;dbl&gt; | Cabin &lt;fct&gt; | Embarked &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n|  1 | FALSE | 3 | male   | 22 | 1 | 0 | A/5 21171        |  7.2500 | <!----> | S |\n|  2 |  TRUE | 1 | female | 38 | 1 | 0 | PC 17599         | 71.2833 | C85  | C |\n|  3 |  TRUE | 3 | female | 26 | 0 | 0 | STON/O2. 3101282 |  7.9250 | <!----> | S |\n|  4 |  TRUE | 1 | female | 35 | 1 | 0 | 113803           | 53.1000 | C123 | S |\n|  5 | FALSE | 3 | male   | 35 | 0 | 0 | 373450           |  8.0500 | <!----> | S |\n|  6 | FALSE | 3 | male   | NA | 0 | 0 | 330877           |  8.4583 | <!----> | Q |\n|  7 | FALSE | 1 | male   | 54 | 0 | 0 | 17463            | 51.8625 | E46  | S |\n|  8 | FALSE | 3 | male   |  2 | 3 | 1 | 349909           | 21.0750 | <!----> | S |\n|  9 |  TRUE | 3 | female | 27 | 0 | 2 | 347742           | 11.1333 | <!----> | S |\n| 10 |  TRUE | 2 | female | 14 | 1 | 0 | 237736           | 30.0708 | <!----> | C |\n\n",
            "text/latex": "A data.frame: 10 × 11\n\\begin{tabular}{r|lllllllllll}\n PassengerId & Survived & Pclass & Sex & Age & SibSp & Parch & Ticket & Fare & Cabin & Embarked\\\\\n <int> & <lgl> & <fct> & <fct> & <dbl> & <int> & <int> & <chr> & <dbl> & <fct> & <fct>\\\\\n\\hline\n\t  1 & FALSE & 3 & male   & 22 & 1 & 0 & A/5 21171        &  7.2500 &      & S\\\\\n\t  2 &  TRUE & 1 & female & 38 & 1 & 0 & PC 17599         & 71.2833 & C85  & C\\\\\n\t  3 &  TRUE & 3 & female & 26 & 0 & 0 & STON/O2. 3101282 &  7.9250 &      & S\\\\\n\t  4 &  TRUE & 1 & female & 35 & 1 & 0 & 113803           & 53.1000 & C123 & S\\\\\n\t  5 & FALSE & 3 & male   & 35 & 0 & 0 & 373450           &  8.0500 &      & S\\\\\n\t  6 & FALSE & 3 & male   & NA & 0 & 0 & 330877           &  8.4583 &      & Q\\\\\n\t  7 & FALSE & 1 & male   & 54 & 0 & 0 & 17463            & 51.8625 & E46  & S\\\\\n\t  8 & FALSE & 3 & male   &  2 & 3 & 1 & 349909           & 21.0750 &      & S\\\\\n\t  9 &  TRUE & 3 & female & 27 & 0 & 2 & 347742           & 11.1333 &      & S\\\\\n\t 10 &  TRUE & 2 & female & 14 & 1 & 0 & 237736           & 30.0708 &      & C\\\\\n\\end{tabular}\n",
            "text/plain": "   PassengerId Survived Pclass Sex    Age SibSp Parch Ticket           Fare   \n1   1          FALSE    3      male   22  1     0     A/5 21171         7.2500\n2   2           TRUE    1      female 38  1     0     PC 17599         71.2833\n3   3           TRUE    3      female 26  0     0     STON/O2. 3101282  7.9250\n4   4           TRUE    1      female 35  1     0     113803           53.1000\n5   5          FALSE    3      male   35  0     0     373450            8.0500\n6   6          FALSE    3      male   NA  0     0     330877            8.4583\n7   7          FALSE    1      male   54  0     0     17463            51.8625\n8   8          FALSE    3      male    2  3     1     349909           21.0750\n9   9           TRUE    3      female 27  0     2     347742           11.1333\n10 10           TRUE    2      female 14  1     0     237736           30.0708\n   Cabin Embarked\n1        S       \n2  C85   C       \n3        S       \n4  C123  S       \n5        S       \n6        Q       \n7  E46   S       \n8        S       \n9        S       \n10       C       "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Veamos la frecuencia de todos los valores de cada columna\nfreqs(dft)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "1 variables with more than 0.9 variance exluded: 'PassengerId'\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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sqoUaNSUlIqPNuMGTOEEJGRkaYvbDD5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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**TRANSFORMACIONES:** Ahora transformamos `Cabin` por su alta varianza y nos deshacemos de `PassengerId` y `Ticket` ya que no tiene mayor sentido usarlos para este ejercicio predictivo."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dft <- dft %>% \n  select(-Ticket, -PassengerId) %>%\n  mutate(Cabin = ifelse(Cabin == \"\", \"NON\", \"WITH\"))",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**ENTENDIMIENTO:** Veamos si existen correlaciones que tengan sentido entre todas las variables:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "corr_cross(dft)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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kBK6N27NwDo6Ohs2LAhLS1NLBY/fPhw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\nYgcAAACgIf4fueyedSFvfisAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "De aquí, sin haber visto la película de Titanic, podemos entender qué pasó: 1era clase cuenta con cabinas (`Cabin`) definidas, pagaron (`Fare`) más y suelen ser mayores de edad (`Age`) que las otras clases. Los hombres (`Sex`) sobrevivieron (`Survived`) en menores proporciones. Los de 3era clase no tienen cabina asignada y pagaron menos. Los que tienen muchos hijos y padres abordo (`parch`) también suelen tener hermanos y cónyuges (`SibSp`), es decir, viajan en familias numerosas.\n\n**VALORES FALTANTES:** Estudiemos los valores nulos o vacíos de nuestros datos:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missingness(dft, full = TRUE, plot = TRUE)",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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h8+/PXXX7e3t+/du7dhh+jo6PPnz+sPyzZt2vTgwYN37tw5duxYQUGBm5vbhg0b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\nCYIdAACATBDsAAAAZIJgBwAAIBP/D3NqhJD44HhKAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Podríamos imputar estos valores vacíos de `Age` (tiene 20% de sus valores faltantes), pero por simplicidad y para considerar en los siguientes experimentos, no lo haremos ahora. Si se quisiera hacer, se puede de la siguiente manera: `dft <- impute(dft)`. Te dejamos ese experimento para que compares resultados ;)\n\n**VARIABLE A MODELAR:** Analicemos la variable que vamos a modelar. Fíjate que la clase más popular y frecuente es la 3era, seguido por 1era. Un poco más de la mitad de los pasajeros se encuentran en 3era clase."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "freqs(dft, Pclass)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A tibble: 3 × 4</caption>\n<thead>\n\t<tr><th scope=col>Pclass</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>3</td><td>491</td><td>55.11</td><td> 55.11</td></tr>\n\t<tr><td>1</td><td>216</td><td>24.24</td><td> 79.35</td></tr>\n\t<tr><td>2</td><td>184</td><td>20.65</td><td>100.00</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA tibble: 3 × 4\n\n| Pclass &lt;fct&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|\n| 3 | 491 | 55.11 |  55.11 |\n| 1 | 216 | 24.24 |  79.35 |\n| 2 | 184 | 20.65 | 100.00 |\n\n",
            "text/latex": "A tibble: 3 × 4\n\\begin{tabular}{r|llll}\n Pclass & n & p & pcum\\\\\n <fct> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 3 & 491 & 55.11 &  55.11\\\\\n\t 1 & 216 & 24.24 &  79.35\\\\\n\t 2 & 184 & 20.65 & 100.00\\\\\n\\end{tabular}\n",
            "text/plain": "  Pclass n   p     pcum  \n1 3      491 55.11  55.11\n2 1      216 24.24  79.35\n3 2      184 20.65 100.00"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## EL EXPERIMENTO\n\nComparemos ahora distintas opciones de modelos bajo una misma métrica global: exactitud (accuracy). La exactitud es la cantidad de casos bien \"adivinados\" vs la cantidad total de casos.\n\n**NOTA:** Todos los mensajes automáticos (muy útiles e informativos) que aparecen cuando corremos el `h2o_automl` serán excluidos de este reporte para tener una mejor y comprensible lectura. No recomiendo dejar `quiet = TRUE` si deseas replicar los ejemplos."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Modelo 1: Modelo balanceado (91.79%)\n\nEn este primer experimento, vamos a modelar balanceando todo el dataset para que hayan igual cantidad de casos para cada clase. Es decir, como 2da clase tiene menos pasajeros, vamos a hacer \"under-sampling\" de las otras clases para que tengan la misma cantidad de pasajeros en cada clase y no exista una sobre representación. El resultado será beneficiado o no dependiendo de cada problema; no hay una regla fija de éxito.\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model1 <- h2o_automl(dft, \"Pclass\", max_models = 4, balance = TRUE, quiet = TRUE)\nmodel1$metrics$plots$conf_matrix",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Model type: Classifier\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "      AUC     ACC\n1 0.98663 0.91791\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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uU+p0FdXX3x4sXh4eGJiYl//fXXrFmz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2nrn18nVsjnwlkEOKFCoY8eqPPwmK2tuJ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\nunEz1caq5hPRz8dzc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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model1$scores_test %>% \n  mutate(correct = tag == score) %>% \n  freqs(score, correct, rel = TRUE) %>% \n  filter(correct == TRUE)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A grouped_df: 3 × 5</caption>\n<thead>\n\t<tr><th scope=col>score</th><th scope=col>correct</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>p3</td><td>TRUE</td><td>133</td><td>97.08</td><td>97.08</td></tr>\n\t<tr><td>p1</td><td>TRUE</td><td> 61</td><td>92.42</td><td>92.42</td></tr>\n\t<tr><td>p2</td><td>TRUE</td><td> 52</td><td>80.00</td><td>80.00</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA grouped_df: 3 × 5\n\n| score &lt;fct&gt; | correct &lt;lgl&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|---|\n| p3 | TRUE | 133 | 97.08 | 97.08 |\n| p1 | TRUE |  61 | 92.42 | 92.42 |\n| p2 | TRUE |  52 | 80.00 | 80.00 |\n\n",
            "text/latex": "A grouped_df: 3 × 5\n\\begin{tabular}{r|lllll}\n score & correct & n & p & pcum\\\\\n <fct> & <lgl> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t p3 & TRUE & 133 & 97.08 & 97.08\\\\\n\t p1 & TRUE &  61 & 92.42 & 92.42\\\\\n\t p2 & TRUE &  52 & 80.00 & 80.00\\\\\n\\end{tabular}\n",
            "text/plain": "  score correct n   p     pcum \n1 p3    TRUE    133 97.08 97.08\n2 p1    TRUE     61 92.42 92.42\n3 p2    TRUE     52 80.00 80.00"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "El resultado es bastante aceptable pero aún no tenemos modelos para comparar. Sigamos con los siguientes modelos pero, primero...\n\nVale la pena ver los resultados de un modelo categórico para comprender mejor cómo funciona el proceso. Como pueden ver, para el test set, tenemos el verdadero valor `tag` y la predicción del modelo `score`. Además, podemos ver una columna adicional por cada una de las categorías; esos valores son la probabilidad de que sea esa etiqueta y, por defecto, se establece como `score` la etiqueta que tenga el máximo valor.\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Veamos los primeros 10 registros del test set\nhead(model1$scores_test, 10) %>%\n    # Veamos que la suma de los scores es igual a 1\n    mutate(sum = p1 + p2 + p3)",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A data.frame: 10 × 6</caption>\n<thead>\n\t<tr><th scope=col>tag</th><th scope=col>score</th><th scope=col>p1</th><th scope=col>p2</th><th scope=col>p3</th><th scope=col>sum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>p3</td><td>p2</td><td>0.009960405</td><td>0.76572067</td><td>0.224318936</td><td>1.0000000</td></tr>\n\t<tr><td>p2</td><td>p2</td><td>0.188104451</td><td>0.56433111</td><td>0.247564390</td><td>1.0000000</td></tr>\n\t<tr><td>p1</td><td>p1</td><td>0.976388991</td><td>0.01959110</td><td>0.004019960</td><td>1.0000000</td></tr>\n\t<tr><td>p3</td><td>p2</td><td>0.162093818</td><td>0.63649893</td><td>0.201407194</td><td>0.9999999</td></tr>\n\t<tr><td>p2</td><td>p2</td><td>0.018646609</td><td>0.87194520</td><td>0.109408237</td><td>1.0000000</td></tr>\n\t<tr><td>p2</td><td>p2</td><td>0.192036077</td><td>0.77061421</td><td>0.037349675</td><td>1.0000000</td></tr>\n\t<tr><td>p3</td><td>p3</td><td>0.004446969</td><td>0.01704985</td><td>0.978503168</td><td>1.0000000</td></tr>\n\t<tr><td>p1</td><td>p1</td><td>0.930631399</td><td>0.06189050</td><td>0.007478118</td><td>1.0000000</td></tr>\n\t<tr><td>p3</td><td>p2</td><td>0.115900457</td><td>0.79557878</td><td>0.088520765</td><td>1.0000000</td></tr>\n\t<tr><td>p3</td><td>p3</td><td>0.017822430</td><td>0.07507523</td><td>0.907102287</td><td>0.9999999</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA data.frame: 10 × 6\n\n| tag &lt;fct&gt; | score &lt;fct&gt; | p1 &lt;dbl&gt; | p2 &lt;dbl&gt; | p3 &lt;dbl&gt; | sum &lt;dbl&gt; |\n|---|---|---|---|---|---|\n| p3 | p2 | 0.009960405 | 0.76572067 | 0.224318936 | 1.0000000 |\n| p2 | p2 | 0.188104451 | 0.56433111 | 0.247564390 | 1.0000000 |\n| p1 | p1 | 0.976388991 | 0.01959110 | 0.004019960 | 1.0000000 |\n| p3 | p2 | 0.162093818 | 0.63649893 | 0.201407194 | 0.9999999 |\n| p2 | p2 | 0.018646609 | 0.87194520 | 0.109408237 | 1.0000000 |\n| p2 | p2 | 0.192036077 | 0.77061421 | 0.037349675 | 1.0000000 |\n| p3 | p3 | 0.004446969 | 0.01704985 | 0.978503168 | 1.0000000 |\n| p1 | p1 | 0.930631399 | 0.06189050 | 0.007478118 | 1.0000000 |\n| p3 | p2 | 0.115900457 | 0.79557878 | 0.088520765 | 1.0000000 |\n| p3 | p3 | 0.017822430 | 0.07507523 | 0.907102287 | 0.9999999 |\n\n",
            "text/latex": "A data.frame: 10 × 6\n\\begin{tabular}{r|llllll}\n tag & score & p1 & p2 & p3 & sum\\\\\n <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t p3 & p2 & 0.009960405 & 0.76572067 & 0.224318936 & 1.0000000\\\\\n\t p2 & p2 & 0.188104451 & 0.56433111 & 0.247564390 & 1.0000000\\\\\n\t p1 & p1 & 0.976388991 & 0.01959110 & 0.004019960 & 1.0000000\\\\\n\t p3 & p2 & 0.162093818 & 0.63649893 & 0.201407194 & 0.9999999\\\\\n\t p2 & p2 & 0.018646609 & 0.87194520 & 0.109408237 & 1.0000000\\\\\n\t p2 & p2 & 0.192036077 & 0.77061421 & 0.037349675 & 1.0000000\\\\\n\t p3 & p3 & 0.004446969 & 0.01704985 & 0.978503168 & 1.0000000\\\\\n\t p1 & p1 & 0.930631399 & 0.06189050 & 0.007478118 & 1.0000000\\\\\n\t p3 & p2 & 0.115900457 & 0.79557878 & 0.088520765 & 1.0000000\\\\\n\t p3 & p3 & 0.017822430 & 0.07507523 & 0.907102287 & 0.9999999\\\\\n\\end{tabular}\n",
            "text/plain": "   tag score p1          p2         p3          sum      \n1  p3  p2    0.009960405 0.76572067 0.224318936 1.0000000\n2  p2  p2    0.188104451 0.56433111 0.247564390 1.0000000\n3  p1  p1    0.976388991 0.01959110 0.004019960 1.0000000\n4  p3  p2    0.162093818 0.63649893 0.201407194 0.9999999\n5  p2  p2    0.018646609 0.87194520 0.109408237 1.0000000\n6  p2  p2    0.192036077 0.77061421 0.037349675 1.0000000\n7  p3  p3    0.004446969 0.01704985 0.978503168 1.0000000\n8  p1  p1    0.930631399 0.06189050 0.007478118 1.0000000\n9  p3  p2    0.115900457 0.79557878 0.088520765 1.0000000\n10 p3  p3    0.017822430 0.07507523 0.907102287 0.9999999"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Modelo 2: Modelo no balanceado (93.28%)\n\nAhora corramos exactamente el mismo modelo pero sin balancear. Tomaremos todos los datos que tenemos para cada una de las 3 clases.\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model2 <- h2o_automl(dft, \"Pclass\", max_models = 4, quiet = TRUE)\nmodel2$metrics$plots$conf_matrix",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Model type: Classifier\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "      AUC     ACC\n1 0.99058 0.93284\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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lNfqnxOg7q6+rJlyyIjI1NSUv7666+5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YmPjw4UOlq8gAR+wAAAD+hv37969fvz4mJnQGKO4AACAASURBVMbLy0tEnj17tmDB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\ngCcQ7AAAAAB4AsEOAAAAgCcQ7AAAAAB4AsEOAP6VtLQ0eV3Gjx+/evXqW7d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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model2$scores_test %>% mutate(correct = tag == score) %>% \n  freqs(score, correct, rel = TRUE) %>% filter(correct == TRUE)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A grouped_df: 3 × 5</caption>\n<thead>\n\t<tr><th scope=col>score</th><th scope=col>correct</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>p3</td><td>TRUE</td><td>139</td><td>95.86</td><td>95.86</td></tr>\n\t<tr><td>p1</td><td>TRUE</td><td> 61</td><td>93.85</td><td>93.85</td></tr>\n\t<tr><td>p2</td><td>TRUE</td><td> 50</td><td>86.21</td><td>86.21</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA grouped_df: 3 × 5\n\n| score &lt;fct&gt; | correct &lt;lgl&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|---|\n| p3 | TRUE | 139 | 95.86 | 95.86 |\n| p1 | TRUE |  61 | 93.85 | 93.85 |\n| p2 | TRUE |  50 | 86.21 | 86.21 |\n\n",
            "text/latex": "A grouped_df: 3 × 5\n\\begin{tabular}{r|lllll}\n score & correct & n & p & pcum\\\\\n <fct> & <lgl> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t p3 & TRUE & 139 & 95.86 & 95.86\\\\\n\t p1 & TRUE &  61 & 93.85 & 93.85\\\\\n\t p2 & TRUE &  50 & 86.21 & 86.21\\\\\n\\end{tabular}\n",
            "text/plain": "  score correct n   p     pcum \n1 p3    TRUE    139 95.86 95.86\n2 p1    TRUE     61 93.85 93.85\n3 p2    TRUE     50 86.21 86.21"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Vemos que los resultados son mejores esta vez. Comparando la exactitud, mejoramos 1.5 puntos porcentuales. Viendo clase por clase, empeoramos un poco en 3era clase (confundiéndolos un poco más con 2da pero menos en 1era), nos mantenemos muy bien con 1era clase y mejoramos 2da clase."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Modelo 3: Modelo como regresión (93.28% - 94.03%)\n\nAhora, modelemos las clases pero como valores numéricos ordenados. Tal y como podrían haber sido clases A, B y C, transformamos las clases a 1, 2 y 3. Luego, podríamos separar con umbrales 1.5 y 2.5 cada una de las 3 etiquetas. Por último, probamos si busando otros valores distintos a 1.5 conseguimos mayor exactitud."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model3 <-  dft %>% mutate(Pclass = as.numeric(Pclass)) %>%\n  h2o_automl(\"Pclass\", max_models = 4, thresh = 0, quiet = TRUE)\nconf1 <- model3$scores_test %>% \n  mutate(label = case_when(\n    score < 1.5 ~ \"1\", \n    score > 2.5 ~ \"3\", \n    TRUE ~ \"2\")) %>% \n  mutate(correct = tag == label)\nmplot_conf(conf1$tag, conf1$label)",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Model type: Regression\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "       rmse       mae       mape        mse    rsq  rsqa\n1 0.2374868 0.1189057 0.04452558 0.05639998 0.9183 0.918\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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C+lyvs0KCoqLlmyJDw8PDEx8dSpU05O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\nW7dDhw6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jjx49srKyVq9e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\nTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "conf1 %>% freqs(tag, correct, rel = TRUE) %>% arrange(tag, desc(correct))",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A grouped_df: 6 × 5</caption>\n<thead>\n\t<tr><th scope=col>tag</th><th scope=col>correct</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>1</td><td> TRUE</td><td> 58</td><td>92.06</td><td> 92.06</td></tr>\n\t<tr><td>1</td><td>FALSE</td><td>  5</td><td> 7.94</td><td>100.00</td></tr>\n\t<tr><td>2</td><td> TRUE</td><td> 51</td><td>91.07</td><td> 91.07</td></tr>\n\t<tr><td>2</td><td>FALSE</td><td>  5</td><td> 8.93</td><td>100.00</td></tr>\n\t<tr><td>3</td><td> TRUE</td><td>141</td><td>94.63</td><td> 94.63</td></tr>\n\t<tr><td>3</td><td>FALSE</td><td>  8</td><td> 5.37</td><td>100.00</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA grouped_df: 6 × 5\n\n| tag &lt;dbl&gt; | correct &lt;lgl&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|---|\n| 1 |  TRUE |  58 | 92.06 |  92.06 |\n| 1 | FALSE |   5 |  7.94 | 100.00 |\n| 2 |  TRUE |  51 | 91.07 |  91.07 |\n| 2 | FALSE |   5 |  8.93 | 100.00 |\n| 3 |  TRUE | 141 | 94.63 |  94.63 |\n| 3 | FALSE |   8 |  5.37 | 100.00 |\n\n",
            "text/latex": "A grouped_df: 6 × 5\n\\begin{tabular}{r|lllll}\n tag & correct & n & p & pcum\\\\\n <dbl> & <lgl> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 1 &  TRUE &  58 & 92.06 &  92.06\\\\\n\t 1 & FALSE &   5 &  7.94 & 100.00\\\\\n\t 2 &  TRUE &  51 & 91.07 &  91.07\\\\\n\t 2 & FALSE &   5 &  8.93 & 100.00\\\\\n\t 3 &  TRUE & 141 & 94.63 &  94.63\\\\\n\t 3 & FALSE &   8 &  5.37 & 100.00\\\\\n\\end{tabular}\n",
            "text/plain": "  tag correct n   p     pcum  \n1 1    TRUE    58 92.06  92.06\n2 1   FALSE     5  7.94 100.00\n3 2    TRUE    51 91.07  91.07\n4 2   FALSE     5  8.93 100.00\n5 3    TRUE   141 94.63  94.63\n6 3   FALSE     8  5.37 100.00"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Interesante: vemos que estos resultados son idénticos que con el modelo anterior.\n\nAhora, podremos mejorar los resultados si logramos conseguir la combinación de umbrales que maximizan la exactitud global de las predicciones. Idealmente, si se tienen más datos que en este caso, se debería validar con un proceso de validación cruzada para evitar sesgos en la optención de estos puntos de corte.\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "a <- seq(from = 1, to = 2, length.out = 10)\nb <- seq(from = 2, to = 3, length.out = 50)\nc <- expand.grid(a,b)\nfor (i in 1:nrow(c)) {\n  if (i == 1) out <- data.frame(a = 0, b = 0, acc = 0)\n  acc <- model3$scores_test %>% \n    mutate(label = case_when(\n      score < c$Var1[i] ~ \"1\", \n      score > c$Var2[i] ~ \"3\", \n      TRUE ~ \"2\")) %>% \n    mutate(correct = tag == label) %>%\n    freqs(correct) %>% filter(correct == TRUE) %>% .$p\n  v <- data.frame(a = c$Var1[i], b = c$Var2[i], acc = acc)\n  if (v$acc > max(out$acc)) out <- rbind(out, v)\n}\nprint(tail(out, 1))",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": "          a        b   acc\n20 1.444444 2.244898 94.03\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "conf2 <- model3$scores_test %>% \n  mutate(label = case_when(\n    score < out$a[nrow(out)] ~ \"1\", \n    score > out$b[nrow(out)] ~ \"3\", \n    TRUE ~ \"2\")) %>% \n  mutate(correct = tag == label)\nmplot_conf(conf2$tag, conf2$label)",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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BkzBg8eXLIr9ovXge6yf//+zMzM8+fPv337ls/nt27d2snJycDAoELXodLCwsJS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\nnByZTJaYmLh//36GYeiCfISQoUOHMgyzcePG58+f5+fnZ2Zm+vj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ktQtnKLsWwB0IdlAtvf2QSgipqSfs275ZbQOdTWduRSWkSqRF4e+S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JIURycrIQwsrKSgjx+vVrxaayZctmXlUh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\n8g1OeHzU7IWfaa8fn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W0NDQkSNHVljR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\nDgAAQCcIdgAAADpBsAMAANCJ/w8qkzCXnpXymAAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "conf2 %>% freqs(tag, correct, rel = TRUE) %>% arrange(tag, desc(correct))",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A grouped_df: 6 × 5</caption>\n<thead>\n\t<tr><th scope=col>tag</th><th scope=col>correct</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>1</td><td> TRUE</td><td> 58</td><td>92.06</td><td> 92.06</td></tr>\n\t<tr><td>1</td><td>FALSE</td><td>  5</td><td> 7.94</td><td>100.00</td></tr>\n\t<tr><td>2</td><td> TRUE</td><td> 48</td><td>85.71</td><td> 85.71</td></tr>\n\t<tr><td>2</td><td>FALSE</td><td>  8</td><td>14.29</td><td>100.00</td></tr>\n\t<tr><td>3</td><td> TRUE</td><td>146</td><td>97.99</td><td> 97.99</td></tr>\n\t<tr><td>3</td><td>FALSE</td><td>  3</td><td> 2.01</td><td>100.00</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA grouped_df: 6 × 5\n\n| tag &lt;dbl&gt; | correct &lt;lgl&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|---|\n| 1 |  TRUE |  58 | 92.06 |  92.06 |\n| 1 | FALSE |   5 |  7.94 | 100.00 |\n| 2 |  TRUE |  48 | 85.71 |  85.71 |\n| 2 | FALSE |   8 | 14.29 | 100.00 |\n| 3 |  TRUE | 146 | 97.99 |  97.99 |\n| 3 | FALSE |   3 |  2.01 | 100.00 |\n\n",
            "text/latex": "A grouped_df: 6 × 5\n\\begin{tabular}{r|lllll}\n tag & correct & n & p & pcum\\\\\n <dbl> & <lgl> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 1 &  TRUE &  58 & 92.06 &  92.06\\\\\n\t 1 & FALSE &   5 &  7.94 & 100.00\\\\\n\t 2 &  TRUE &  48 & 85.71 &  85.71\\\\\n\t 2 & FALSE &   8 & 14.29 & 100.00\\\\\n\t 3 &  TRUE & 146 & 97.99 &  97.99\\\\\n\t 3 & FALSE &   3 &  2.01 & 100.00\\\\\n\\end{tabular}\n",
            "text/plain": "  tag correct n   p     pcum  \n1 1    TRUE    58 92.06  92.06\n2 1   FALSE     5  7.94 100.00\n3 2    TRUE    48 85.71  85.71\n4 2   FALSE     8 14.29 100.00\n5 3    TRUE   146 97.99  97.99\n6 3   FALSE     3  2.01 100.00"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Con estos nuevos cortes, vemos que 2da clase empeora (3 pasajeros) pero 3era mejora (5 pasajeros). Se sacrifica un poco la calidad de predicción pero se tiene mayor exactitud global. 1era clase queda intacta.\n\nVeámoslo gráficamente:"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "conf2 %>% mutate(tag = paste(\"Clase\", tag)) %>%\n  ggplot(aes(x = tag, y = score, colour = correct)) +\n  geom_crossbar(ymin = 0, ymax = 4, alpha = 0.5) +\n  geom_jitter(colour = \"black\", alpha = 0.5) +\n  geom_hline(yintercept = 1.5, linetype = \"dotted\") +\n  geom_hline(yintercept = 2.5, linetype = \"dotted\") +\n  geom_hline(yintercept = out$a[nrow(out)]) +\n  geom_hline(yintercept = out$b[nrow(out)]) +\n  labs(title = \"Regresión lineal (cortes optimizados)\", \n       subtitle = \"Línea punteada: cortes triviales. Línea continua: cortes maximizados.\",\n       x = NULL, y = \"Predicción\") +\n  guides(colour = FALSE) + \n  facet_grid(tag~., scales = \"free\") +\n  theme_lares2() + coord_flip() +\n  theme(axis.text.y = element_blank(),\n        axis.title.y = element_blank(),\n        panel.grid.major = element_blank(),\n        panel.grid.minor = element_blank())",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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Zdf1q9fz32LbN269eLFi4GBgc8++2xCQoJcLk9LS/vmm282bdqUkJCgHwPt3Llz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\nRsMLkpmd+nUAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Por curiosidad, veamos los más extremos outliers de clase 2:\nmodel3$datasets$test %>% \n    mutate(score = model3$scores_test$score) %>% \n    filter(tag == 2) %>%\n    filter(score == max(score) | score == min(score))",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A data.frame: 2 × 10</caption>\n<thead>\n\t<tr><th scope=col>Survived</th><th scope=col>tag</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th><th scope=col>Cabin</th><th scope=col>Embarked</th><th scope=col>score</th></tr>\n\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>FALSE</td><td>2</td><td>male  </td><td>36.5</td><td>0</td><td>2</td><td>26.00</td><td>WITH</td><td>S</td><td>1.184893</td></tr>\n\t<tr><td> TRUE</td><td>2</td><td>female</td><td>24.0</td><td>2</td><td>3</td><td>18.75</td><td>NON </td><td>S</td><td>2.969187</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA data.frame: 2 × 10\n\n| Survived &lt;lgl&gt; | tag &lt;dbl&gt; | Sex &lt;fct&gt; | Age &lt;dbl&gt; | SibSp &lt;int&gt; | Parch &lt;int&gt; | Fare &lt;dbl&gt; | Cabin &lt;fct&gt; | Embarked &lt;fct&gt; | score &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|\n| FALSE | 2 | male   | 36.5 | 0 | 2 | 26.00 | WITH | S | 1.184893 |\n|  TRUE | 2 | female | 24.0 | 2 | 3 | 18.75 | NON  | S | 2.969187 |\n\n",
            "text/latex": "A data.frame: 2 × 10\n\\begin{tabular}{r|llllllllll}\n Survived & tag & Sex & Age & SibSp & Parch & Fare & Cabin & Embarked & score\\\\\n <lgl> & <dbl> & <fct> & <dbl> & <int> & <int> & <dbl> & <fct> & <fct> & <dbl>\\\\\n\\hline\n\t FALSE & 2 & male   & 36.5 & 0 & 2 & 26.00 & WITH & S & 1.184893\\\\\n\t  TRUE & 2 & female & 24.0 & 2 & 3 & 18.75 & NON  & S & 2.969187\\\\\n\\end{tabular}\n",
            "text/plain": "  Survived tag Sex    Age  SibSp Parch Fare  Cabin Embarked score   \n1 FALSE    2   male   36.5 0     2     26.00 WITH  S        1.184893\n2  TRUE    2   female 24.0 2     3     18.75 NON   S        2.969187"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Modelo 4: Otro algoritmo, categóricos ordenados (84,70%)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dft2 <- impute(dft, quiet = TRUE) # MASS::polr no admite valores nulos\ndft2$Pclass <- factor(dft2$Pclass, ordered = TRUE)\nsplit <- msplit(dft2, print = FALSE)\nmodel4 <- MASS::polr(Pclass ~ ., data = split$train, Hess = TRUE)\nsummary(model4)\nsplit$test$pred <- predict(model4, split$test)\n100*sum(as.character(split$test$Pclass) == as.character(split$test$pred))/nrow(split$test)",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "Call:\nMASS::polr(formula = Pclass ~ ., data = split$train, Hess = TRUE)\n\nCoefficients:\n                Value Std. Error t value\nSurvivedTRUE -1.05394   0.299526  -3.519\nSexmale      -0.45979   0.309646  -1.485\nAge          -0.04891   0.009202  -5.315\nSibSp         1.00288   0.227004   4.418\nParch         0.51841   0.179020   2.896\nFare         -0.12581   0.013795  -9.120\nCabinWITH    -2.81847   0.409023  -6.891\nEmbarkedC     6.87947   0.373512  18.418\nEmbarkedQ    10.01670   0.811359  12.346\nEmbarkedS     6.83830   0.326874  20.920\n\nIntercepts:\n    Value   Std. Error t value\n1|2 -0.6988  0.5453    -1.2817\n2|3  2.6331  0.4284     6.1462\n\nResidual Deviance: 544.967 \nAIC: 568.967 "
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": "84.7014925373134",
            "text/markdown": "84.7014925373134",
            "text/latex": "84.7014925373134",
            "text/plain": "[1] 84.70149"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "No vale la pena continuar con este algoritmo por su mal desempeño para este caso en particular."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Modelo 5: Oversampling (96.28% con sesgo)\nPara cierta clase, normalmente la que tenga menor volumen de datos, se le crea observaciones nuevas haciendo sampling con reemplazamiento y, a las de mayor volumen, se les deja igual. La creación de nuevos casos de esta clase incrementa su efecto en el modelo de clasificación.\n\n> SMOTE: Synthetic Minority Over-sampling Technique"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "set.seed(123)\n# Para la librería DMwR, todos los valores categóricos deben ser factors\ndfx <- dft %>% mutate(Pclass = as.factor(Pclass), Cabin = as.factor(Cabin))\ndf.smote <- SMOTE(Pclass ~ ., dfx, perc.over = 150, perc.under = 100)\nfreqs(df.smote, Pclass)\n# Como esta librería que use está diseñada puntualmente para solo 2 clases pero sí \n# crea las observaciones como necesitamos, nos quedaremos sólo las creadas para Pclass == 2\nprint(nrow(distinct(df.smote[df.smote$Pclass == 2,])) %>% \n  paste(., \"casos únicos para 2da clase; antes teníamos\", \n        length(dft$Pclass[dft$Pclass == 2])))\n# Juntamos ahora la clase 2 con el resto para entrenar el modelo\ndf2 <- dft %>% filter(Pclass != 2) %>% \n  rbind(df.smote %>% filter(Pclass == 2))\nfreqs(df2, Pclass)",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A tibble: 3 × 4</caption>\n<thead>\n\t<tr><th scope=col>Pclass</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>2</td><td>368</td><td>66.67</td><td> 66.67</td></tr>\n\t<tr><td>3</td><td>129</td><td>23.37</td><td> 90.04</td></tr>\n\t<tr><td>1</td><td> 55</td><td> 9.96</td><td>100.00</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA tibble: 3 × 4\n\n| Pclass &lt;fct&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|\n| 2 | 368 | 66.67 |  66.67 |\n| 3 | 129 | 23.37 |  90.04 |\n| 1 |  55 |  9.96 | 100.00 |\n\n",
            "text/latex": "A tibble: 3 × 4\n\\begin{tabular}{r|llll}\n Pclass & n & p & pcum\\\\\n <fct> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 2 & 368 & 66.67 &  66.67\\\\\n\t 3 & 129 & 23.37 &  90.04\\\\\n\t 1 &  55 &  9.96 & 100.00\\\\\n\\end{tabular}\n",
            "text/plain": "  Pclass n   p     pcum  \n1 2      368 66.67  66.67\n2 3      129 23.37  90.04\n3 1       55  9.96 100.00"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "[1] \"347 casos únicos para 2da clase; antes teníamos 184\"\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A tibble: 3 × 4</caption>\n<thead>\n\t<tr><th scope=col>Pclass</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>3</td><td>491</td><td>45.67</td><td>45.67</td></tr>\n\t<tr><td>2</td><td>368</td><td>34.23</td><td>79.90</td></tr>\n\t<tr><td>1</td><td>216</td><td>20.09</td><td>99.99</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA tibble: 3 × 4\n\n| Pclass &lt;fct&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|\n| 3 | 491 | 45.67 | 45.67 |\n| 2 | 368 | 34.23 | 79.90 |\n| 1 | 216 | 20.09 | 99.99 |\n\n",
            "text/latex": "A tibble: 3 × 4\n\\begin{tabular}{r|llll}\n Pclass & n & p & pcum\\\\\n <fct> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t 3 & 491 & 45.67 & 45.67\\\\\n\t 2 & 368 & 34.23 & 79.90\\\\\n\t 1 & 216 & 20.09 & 99.99\\\\\n\\end{tabular}\n",
            "text/plain": "  Pclass n   p     pcum \n1 3      491 45.67 45.67\n2 2      368 34.23 79.90\n3 1      216 20.09 99.99"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model5.1 <- h2o_automl(df2, \"Pclass\", max_models = 4, balance = FALSE, quiet = TRUE) # Mejor\n# model5.2 <- h2o_automl(df2, \"Pclass\", max_models = 4, balance = TRUE) # Peor\nmodel5.1$metrics$plots$conf_matrix",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Model type: Classifier\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "      AUC     ACC\n1 0.99553 0.96285\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "plot without title",
            "image/png": 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Q3q6urz58+Pjo5OSkr666+/PDw8LCws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9W9YdtfCPUkuNupL8Qhtvfw+nk1eSH8mx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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model5.1$scores_test %>% mutate(correct = tag == score) %>% \n  freqs(score, correct, rel = TRUE)",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<table>\n<caption>A grouped_df: 5 × 5</caption>\n<thead>\n\t<tr><th scope=col>score</th><th scope=col>correct</th><th scope=col>n</th><th scope=col>p</th><th scope=col>pcum</th></tr>\n\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>p3</td><td> TRUE</td><td>149</td><td> 98.03</td><td> 98.03</td></tr>\n\t<tr><td>p2</td><td> TRUE</td><td> 94</td><td> 91.26</td><td> 91.26</td></tr>\n\t<tr><td>p1</td><td> TRUE</td><td> 68</td><td>100.00</td><td>100.00</td></tr>\n\t<tr><td>p2</td><td>FALSE</td><td>  9</td><td>  8.74</td><td>100.00</td></tr>\n\t<tr><td>p3</td><td>FALSE</td><td>  3</td><td>  1.97</td><td>100.00</td></tr>\n</tbody>\n</table>\n",
            "text/markdown": "\nA grouped_df: 5 × 5\n\n| score &lt;fct&gt; | correct &lt;lgl&gt; | n &lt;int&gt; | p &lt;dbl&gt; | pcum &lt;dbl&gt; |\n|---|---|---|---|---|\n| p3 |  TRUE | 149 |  98.03 |  98.03 |\n| p2 |  TRUE |  94 |  91.26 |  91.26 |\n| p1 |  TRUE |  68 | 100.00 | 100.00 |\n| p2 | FALSE |   9 |   8.74 | 100.00 |\n| p3 | FALSE |   3 |   1.97 | 100.00 |\n\n",
            "text/latex": "A grouped_df: 5 × 5\n\\begin{tabular}{r|lllll}\n score & correct & n & p & pcum\\\\\n <fct> & <lgl> & <int> & <dbl> & <dbl>\\\\\n\\hline\n\t p3 &  TRUE & 149 &  98.03 &  98.03\\\\\n\t p2 &  TRUE &  94 &  91.26 &  91.26\\\\\n\t p1 &  TRUE &  68 & 100.00 & 100.00\\\\\n\t p2 & FALSE &   9 &   8.74 & 100.00\\\\\n\t p3 & FALSE &   3 &   1.97 & 100.00\\\\\n\\end{tabular}\n",
            "text/plain": "  score correct n   p      pcum  \n1 p3     TRUE   149  98.03  98.03\n2 p2     TRUE    94  91.26  91.26\n3 p1     TRUE    68 100.00 100.00\n4 p2    FALSE     9   8.74 100.00\n5 p3    FALSE     3   1.97 100.00"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "- Notar que los volúmenes para cada etiqueta cambian porque tenemos un nuevo dataset con más filas.\n- Ahora no tenemos ni un error entre las clases 1era y 3era (extremos); antes teníamos 1! Tampoco hay casos de mala clasificación entre 1era y 2da clase.\n- Además, ahora somos mejores prediciendo 1era y 2da clase de lo que éramos antes, pero sacrificando un poco la exactitud en 3era clase."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# RESULTADOS\n\n| Modelo  \t| Nombre del modelo           \t| Exactitud \t|\n|:--------:\t|:----------------------------:\t|:---------:\t|\n| #1        \t| Balanceado                   \t|   91.79   \t|\n| #2       \t| Sin balancear                \t|   93.27   \t|\n| #3.1   \t  | Regresión                    \t|   93.30   \t|\n| #3.2        | Regresión cortes óptimos      | 94.03  \t|\n| #4     \t  | Ordered Logistic con MASS    \t|   84.70   \t|\n| #5    \t  | Oversampling (sesgado)       \t|   96.28   \t|\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# RECOMENDACIONES Y CONCLUSIONES\n\n- En todo momento nos tenemos que preguntar: en qué queremos ser mejores? Separando 2da de 3era clase? O mejor separar 3ra y 2da clase de la 1era? Queremos clasificar todos lo mejor posible pero sacrificando una clase en particular?\n\n- Cada caso es único así que no hay una respuesta correcta; se recomienda probar distintas combinaciones antes de llegar al modelo final.\n\n- Si tenemos dos categorías muy similares, consideremos unirlas si es irrelevante hacerlo. Si no, se podría hacer un segundo modelo para poder diferenciarlos dado cierto umbral o volumen. Así, cuando ciertas observaciones pasen ese umbral, podemos pasarlos por un segundo modelo que es \"mejor\" clasificando o separandos esas otras categorías.\n\n- Estos valores de exactitud dependen totalmente de los datos, el volumen y las proporciones entre cada una de las categorías. Si tenemos 5% de un caso, 5% de otro y 90% de otro, sin duda tendremos que balancear de alguna forma para que esos 5% no parezcan ruido ante el modelo y los trate por igual. \n\n- Del mismo modo, y lo digo con pinzas, si queremos que el modelo sea \"mejor\" prediciendo una categoría, podemos mostrarle más observaciones de esa cateogría.\n\n- Por otro lado, si tenemos distintos costos por confundirnos en ciertas categorías, se puede cambiar la lógica de \"el máximo score entre las etiquetas será la predicción final\". Seremos menos exactos en los resultados pero nos confundiremos menos en nuestra variable deseada.\n\n- Para el próximo experimento debería escoger un dataset más grande para evitar mucha varianza y quitarle algunas variables para hacérselo más difícil al modelo y evitar métricas cercanas al resultado óptimo.\n"
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/caa461e43c391ae07094c6495dedb977"
    },
    "gist": {
      "id": "caa461e43c391ae07094c6495dedb977",
      "data": {
        "description": "Modelos Categóricos Ordinales",
        "public": true
      }
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R",
      "language": "R"
    },
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Modelos Categóricos Ordinales.ipynb