explainability_interpretable_models.ipynb

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      "source": [
        "## Machine Learning Explainability: Interpretable Models\n",
        "\n",
        "*Prepared by:*  \n",
        "**Jude Michael Teves**  \n",
        "Faculty, Software Technology Department  \n",
        "College of Computer Studies - De La Salle University"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dise0P70fJ50"
      },
      "source": [
        "## Preliminaries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWXmzEUxfQAw"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        "id": "ySjUVM8JfA52",
        "outputId": "b819fe95-0c0b-44aa-eb9b-928220bd1b6f"
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      "outputs": [
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          "execution_count": 1,
          "metadata": {},
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      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "\n",
        "sns.set_palette('Set2')\n",
        "sns.color_palette('Set2')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2u9zQHFu2-u"
      },
      "source": [
        "We will be using the Titanic dataset in this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "xOq2xJJdu2-u",
        "outputId": "43634c1f-7e5d-4163-eb01-cd9a3dd65c15"
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      "outputs": [
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            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/Cyntwikip/data-repository/main/titanic.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auuaZDJJu2-v",
        "outputId": "38b07342-c7e9-4afc-e424-3808d68d2770"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Set Size | Input: (712, 8), Output: (712,)\n",
            "Test Set Size | Input: (179, 8), Output: (179,)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "X = df.drop(['Survived','PassengerId','Name','Ticket','Cabin'], axis=1)\n",
        "X = pd.get_dummies(X, drop_first=True)\n",
        "y = df['Survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "cols = X_train.columns\n",
        "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imp_mean.fit(X_train)\n",
        "X_train = pd.DataFrame(imp_mean.transform(X_train), columns=cols)\n",
        "X_test = pd.DataFrame(imp_mean.transform(X_test), columns=cols)\n",
        "\n",
        "print(f\"Train Set Size | Input: {X_train.shape}, Output: {y_train.shape}\")\n",
        "print(f\"Test Set Size | Input: {X_test.shape}, Output: {y_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEVNggagu2-w"
      },
      "source": [
        "## Intrepretable Models\n",
        "\n",
        "To achieve model interpretability, one of the simplest approaches is to use models that are inherently interpretable. Common models in this category include linear regression, logistic regression, and decision trees."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdNlYV2wu2-x"
      },
      "source": [
        "### Linear\n",
        "\n",
        "Linear regression models predict the target variable as a weighted sum of the input features. This linearity makes interpretation straightforward, which is why these models have been used for decades by statisticians and data scientists alike. For a single instance, the relationship is expressed as:\n",
        "\n",
        "$$y = \\beta_0 + \\beta_1 x_1 + ... + \\beta_p x_p + \\epsilon$$\n",
        "\n",
        "In this equation, each feature $x_i$ contributes to the outcome $y$ by its corresponding weight $\\beta_i$. Linear models, however, are limited to capturing only linear relationships. If there are nonlinear interactions, they must be manually added as features.\n",
        "\n",
        "Also take note of linear regression assumptions: linearity, homoscedasticity, normality of errors, and no multicollinearity.\n",
        "\n",
        "#### Interpretation\n",
        "\n",
        "As mentioned, this is very straightforward. For every one-unit increase in a feature $x_i$, the predicted outcome changes by $\\beta_i$.\n",
        "\n",
        "In the dataset we are using, we will use Logistic Regression as this is a classification problem but the same concept applies for both Linear and Logistic Regression.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "NuhThTdGu2-x",
        "outputId": "81d30e7b-ea2b-4968-cea3-f2f79fe8cf3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Score: 0.8100558659217877\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.122951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked_Q</th>\n",
              "      <td>-0.043312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.097871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked_S</th>\n",
              "      <td>-0.198737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.350138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.405348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.787073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex_male</th>\n",
              "      <td>-1.274988</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ],
            "text/plain": [
              "Fare          0.122951\n",
              "Embarked_Q   -0.043312\n",
              "Parch        -0.097871\n",
              "Embarked_S   -0.198737\n",
              "SibSp        -0.350138\n",
              "Age          -0.405348\n",
              "Pclass       -0.787073\n",
              "Sex_male     -1.274988\n",
              "dtype: float64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# code here\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train), columns=cols)\n",
        "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=cols)\n",
        "\n",
        "model = LogisticRegression(random_state=42)\n",
        "model.fit(X_train_scaled, y_train)\n",
        "print(\"Score:\", model.score(X_test_scaled, y_test))\n",
        "pd.Series(model.coef_[0], index=model.feature_names_in_).sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-rikc_plu2-x"
      },
      "source": [
        "#### Feature Importance\n",
        "\n",
        "We can check the most important features by looking at the absolute values of the coefficients."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "vsECGa3Bu2-x",
        "outputId": "54ff42d2-c97a-45d2-e634-3192b0000896"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Embarked_Q</th>\n",
              "      <td>-0.043312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>-0.097871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.122951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked_S</th>\n",
              "      <td>-0.198737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.350138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.405348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.787073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex_male</th>\n",
              "      <td>-1.274988</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ],
            "text/plain": [
              "Embarked_Q   -0.043312\n",
              "Parch        -0.097871\n",
              "Fare          0.122951\n",
              "Embarked_S   -0.198737\n",
              "SibSp        -0.350138\n",
              "Age          -0.405348\n",
              "Pclass       -0.787073\n",
              "Sex_male     -1.274988\n",
              "dtype: float64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# model.predict_proba(X_test)\n",
        "# model.predict(X_test)\n",
        "coefs = pd.Series(model.coef_[0], index=model.feature_names_in_)\n",
        "sorted_coefs = coefs.abs().sort_values(ascending=True)\n",
        "coefs[sorted_coefs.index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "5u2Peyabu2-y",
        "outputId": "37399585-6098-4a0f-8a6c-a510db6a2cf8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Feature Importance')"
            ]
          },
          "execution_count": 6,
          "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": [
        "coefs[sorted_coefs.index].plot(kind='barh')\n",
        "plt.xlabel('weight')\n",
        "plt.title('Feature Importance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPrJoOT9u2-y"
      },
      "source": [
        "As you can see, if the sex is male, the survival rate decreases significantly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLQZGjjru2-y"
      },
      "source": [
        "### Tree-based Models\n",
        "\n",
        "Linear models struggle when the relationships between features and outcomes are nonlinear. In these cases, decision trees shine. A decision tree splits the data based on certain feature thresholds, forming subsets until reaching terminal nodes (leaf nodes). Each leaf node predicts an outcome based on the average of the training data within that node."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CaV4AWeWu2-y",
        "outputId": "d7dc23e6-1ce5-40aa-d7f2-eb74708b0215"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Score: 0.7988826815642458\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "print(\"Score:\", model.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNPME6NMu2-y"
      },
      "source": [
        "#### Interpretation\n",
        "\n",
        "Interpreting a decision tree is intuitive. Starting at the root, we follow branches based on the feature values, eventually reaching a leaf node that gives the prediction. The interpretation follows an \"AND\" logic at each split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        },
        "id": "OFsihUpTu2-y",
        "outputId": "b0108e84-647a-4211-897a-dce8e69adaad"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 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-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 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-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-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",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-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",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=42)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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(max_depth=3, random_state=42)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=42)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "bDkWsezKu2-z",
        "outputId": "52dbca8d-d5d7-41f7-c434-9ead10741cce"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn import tree\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "tree.plot_tree(model, feature_names=list(cols), class_names=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRuVBdRuu2-z"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "#### Feature Importance\n",
        "\n",
        "The importance of a feature in a decision tree is determined by how much it reduces the impurity across all splits that use that feature. The importances are normalized to sum to 100%, allowing interpretation of each feature's relative contribution to the overall model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "-R1Wzqhlu2-z",
        "outputId": "0cd602dd-d7b9-4ef9-ca42-f97608e99137"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked_Q</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked_S</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0.048135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.061240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.075353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0.209536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex_male</th>\n",
              "      <td>0.605737</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ],
            "text/plain": [
              "Parch         0.000000\n",
              "Embarked_Q    0.000000\n",
              "Embarked_S    0.000000\n",
              "SibSp         0.048135\n",
              "Fare          0.061240\n",
              "Age           0.075353\n",
              "Pclass        0.209536\n",
              "Sex_male      0.605737\n",
              "dtype: float64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "coefs = pd.Series(model.feature_importances_, index=model.feature_names_in_)\n",
        "sorted_coefs = coefs.abs().sort_values(ascending=True)\n",
        "coefs[sorted_coefs.index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "wwXsRgxdu2-z",
        "outputId": "6ffdeb5e-175a-48fd-9f26-294766bc85ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Feature Importance')"
            ]
          },
          "execution_count": 12,
          "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": [
        "coefs[sorted_coefs.index].plot(kind='barh')\n",
        "plt.xlabel('importance')\n",
        "plt.title('Feature Importance')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtA18qZ0fBXN"
      },
      "source": [
        "## End\n",
        "<sup>made by **Jude Michael Teves**</sup> <br>\n",
        "<sup>for comments, corrections, suggestions, please email:</sup><sup> <href>judemichaelteves@gmail.com</href> or <href>jude.teves@dlsu.edu.ph</href></sup><br>\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "oLj3Z3PucRRd"
      ],
      "name": "interpretable_models.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
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