From c7ad221b8bc844f3f03f095d3b4adbe0eeb919b1 Mon Sep 17 00:00:00 2001 From: DimaRus05 Date: Mon, 24 Nov 2025 18:05:12 +0300 Subject: [PATCH] First homework solution --- adult-census-income.ipynb | 978 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 978 insertions(+) create mode 100644 adult-census-income.ipynb diff --git a/adult-census-income.ipynb b/adult-census-income.ipynb new file mode 100644 index 0000000..7620b85 --- /dev/null +++ b/adult-census-income.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "70beded9", + "metadata": {}, + "source": [ + "**Датасет:** `scikit-learn/adult-census-income`\n", + "\n", + "**Описание:** Попытаемся предсказать, зарабатывает ли человек > 50K$/год по социально-демографическим признакам (возраст, образование, профессия, часы работы и т.д.).\n", + "\n", + "- Тип задачи: бинарная классификация.\n", + "- Целевая переменная: `income`/`label`.\n", + "- Метрики: Accuracy, F1-macro." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "65376130", + "metadata": {}, + "outputs": [], + "source": [ + "# Импорты и базовые настройки\n", + "from datasets import load_dataset\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import display\n", + "\n", + "sns.set_theme(context=\"talk\", style=\"whitegrid\")\n", + "pd.set_option('display.max_columns', 100)" + ] + }, + { + "cell_type": "markdown", + "id": "a7370021", + "metadata": {}, + "source": [ + "# Блок 1. Загрузка и первичный осмотр" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5fd19014", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Сплиты и количество объектов: {'train': 32561}\n", + "\n", + "Схема признаков (HF features):\n", + "{'age': Value('int64'), 'workclass': Value('string'), 'fnlwgt': Value('int64'), 'education': Value('string'), 'education.num': Value('int64'), 'marital.status': Value('string'), 'occupation': Value('string'), 'relationship': Value('string'), 'race': Value('string'), 'sex': Value('string'), 'capital.gain': Value('int64'), 'capital.loss': Value('int64'), 'hours.per.week': Value('int64'), 'native.country': Value('string'), 'income': Value('string')}\n", + "\n", + "Размер таблицы (строки, колонки): (32561, 15)\n", + "Колонки: ['age', 'capital.gain', 'capital.loss', 'education', 'education.num', 'fnlwgt', 'hours.per.week', 'income', 'marital.status', 'native.country', 'occupation', 'race', 'relationship', 'sex', 'workclass']\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset_dict = load_dataset(\"scikit-learn/adult-census-income\")\n", + "\n", + "print(\"Сплиты и количество объектов:\", {split_name: len(dataset_dict[split_name]) for split_name in dataset_dict})\n", + "\n", + "print(\"\\nСхема признаков (HF features):\")\n", + "print(dataset_dict[\"train\"].features)\n", + "\n", + "df = dataset_dict[\"train\"].to_pandas()\n", + "print(\"\\nРазмер таблицы (строки, колонки):\", df.shape)\n", + "print(\"Колонки:\", sorted(df.columns.tolist()))\n", + "\n", + "all_columns = set(df.columns)" + ] + }, + { + "cell_type": "markdown", + "id": "8da0ea4f", + "metadata": {}, + "source": [ + "## Приведение пропусков и выбор ключевых колонок\n", + "В Adult часть пропусков обозначена как строка `'?'`. Заменим такие значения на `NaN`,\n", + "и аккуратно зафиксируем целевую и признаковые колонки." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "84fb7bc2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Целевая переменная: income\n", + "Количество признаков: 14\n" + ] + } + ], + "source": [ + "object_cols = df.select_dtypes(include=\"object\").columns.tolist()\n", + "for c in object_cols:\n", + " df[c] = df[c].replace('?', np.nan)\n", + "\n", + "if 'label' in df.columns:\n", + " target_column = 'label'\n", + "elif 'income' in df.columns:\n", + " target_column = 'income'\n", + "else:\n", + " raise ValueError(\"Не найдена целевая колонка 'label' или 'income'.\")\n", + "\n", + "feature_columns = [c for c in df.columns if c != target_column]\n", + "\n", + "print(\"Целевая переменная:\", target_column)\n", + "print(\"Количество признаков:\", len(feature_columns))" + ] + }, + { + "cell_type": "markdown", + "id": "0afae47d", + "metadata": {}, + "source": [ + "# Блок 2. Первичная диагностика данных (EDA)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0926ba37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество пропусков по колонкам:\n", + "\n", + "age 0\n", + "workclass 1836\n", + "fnlwgt 0\n", + "education 0\n", + "education.num 0\n", + "marital.status 0\n", + "occupation 1843\n", + "relationship 0\n", + "race 0\n", + "sex 0\n", + "capital.gain 0\n", + "capital.loss 0\n", + "hours.per.week 0\n", + "native.country 583\n", + "income 0\n", + "dtype: int64\n", + "\n", + "Числовые признаки (пример): ['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week']\n", + "Категориальные признаки (пример): ['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country']\n", + "\n", + "Описание числовых признаков:\n", + "\n" + ] + }, + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "object", + "type": "string" + }, + { + "name": "mean", + "rawType": "float64", + "type": "float" + }, + { + "name": "std", + "rawType": "float64", + "type": "float" + }, + { + "name": "min", + "rawType": "float64", + "type": "float" + }, + { + "name": "max", + "rawType": "float64", + "type": "float" + } + ], + "ref": "51d8117d-4906-4063-a4d6-3a7bd1fd3290", + "rows": [ + [ + "age", + "38.58", + "13.64", + "17.0", + "90.0" + ], + [ + "fnlwgt", + "189778.37", + "105549.98", + "12285.0", + "1484705.0" + ], + [ + "education.num", + "10.08", + "2.57", + "1.0", + "16.0" + ], + [ + "capital.gain", + "1077.65", + "7385.29", + "0.0", + "99999.0" + ], + [ + "capital.loss", + "87.3", + "402.96", + "0.0", + "4356.0" + ], + [ + "hours.per.week", + "40.44", + "12.35", + "1.0", + "99.0" + ] + ], + "shape": { + "columns": 4, + "rows": 6 + } + }, + "text/html": [ + "
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" + ], + "text/plain": [ + " mean std min max\n", + "age 38.58 13.64 17.0 90.0\n", + "fnlwgt 189778.37 105549.98 12285.0 1484705.0\n", + "education.num 10.08 2.57 1.0 16.0\n", + "capital.gain 1077.65 7385.29 0.0 99999.0\n", + "capital.loss 87.30 402.96 0.0 4356.0\n", + "hours.per.week 40.44 12.35 1.0 99.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Распределение классов в target:\n", + "\n", + "income\n", + "<=50K 0.759\n", + ">50K 0.241\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "print(\"Количество пропусков по колонкам:\\n\")\n", + "print(df[feature_columns + [target_column]].isna().sum())\n", + "\n", + "numeric_columns = df[feature_columns].select_dtypes(include=\"number\").columns.tolist()\n", + "categorical_columns = df[feature_columns].select_dtypes(exclude=\"number\").columns.tolist()\n", + "print(\"\\nЧисловые признаки (пример):\", numeric_columns[:8])\n", + "print(\"Категориальные признаки (пример):\", categorical_columns[:8])\n", + "\n", + "if len(numeric_columns) > 0:\n", + " stats_table = (df[numeric_columns].describe().T[[\"mean\", \"std\", \"min\", \"max\"]].round(2))\n", + " print(\"\\nОписание числовых признаков:\\n\")\n", + " display(stats_table)\n", + "else:\n", + " print(\"Числовых признаков не найдено.\")\n", + "\n", + "print(\"\\nРаспределение классов в target:\\n\")\n", + "print(df[target_column].value_counts(normalize=True).round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "45c4a3d7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,4))\n", + "sns.countplot(x=target_column, data=df)\n", + "plt.title(\"Распределение target\")\n", + "plt.show()\n", + "\n", + "if 'hours-per-week' in df.columns:\n", + " plt.figure(figsize=(7,4))\n", + " sns.kdeplot(data=df, x='hours-per-week', hue=target_column, common_norm=False, fill=True, alpha=0.3)\n", + " plt.title(\"Hours-per-week vs label\")\n", + " plt.show()\n", + "\n", + "if 'sex' in df.columns:\n", + " plt.figure(figsize=(6,4))\n", + " sns.countplot(x='sex', hue=target_column, data=df)\n", + " plt.title(\"Sex vs label\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "73839355", + "metadata": {}, + "source": [ + "# Блок 3. Подготовка данных\n", + "- Заполним пропуски: числовые — медианой, категориальные — модой. \n", + "- Отделим признаки `X` и цель `y`. \n", + "- Разобьём на train/test со стратификацией." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a01568a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Числовые признаки: ['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week']\n", + "Категориальные признаки: ['workclass', 'education', 'marital.status', 'occupation', 'relationship', 'race', 'sex', 'native.country']\n", + "\n", + "Осталось пропусков: 0\n", + "Формы X, y: (32561, 14) (32561,)\n", + "X_train/X_test: (22792, 14) (9769, 14)\n", + "\n", + "Баланс классов в y_train:\n", + " <=50K: 75.9%\n", + " >50K: 24.1%\n", + "\n", + "Баланс классов в y_test:\n", + " <=50K: 75.9%\n", + " >50K: 24.1%\n" + ] + } + ], + "source": [ + "prepared = df[feature_columns + [target_column]].copy()\n", + "features_numeric = prepared[feature_columns].select_dtypes(include=\"number\").columns.tolist()\n", + "features_categorical = prepared[feature_columns].select_dtypes(exclude=\"number\").columns.tolist()\n", + "print(\"Числовые признаки:\", features_numeric[:10])\n", + "print(\"Категориальные признаки:\", features_categorical[:10])\n", + "\n", + "for col in features_numeric:\n", + " prepared[col] = prepared[col].fillna(prepared[col].median())\n", + "\n", + "for col in features_categorical:\n", + " mode_vals = prepared[col].mode(dropna=True)\n", + " if len(mode_vals) > 0:\n", + " prepared[col] = prepared[col].fillna(mode_vals.iloc[0])\n", + "\n", + "print(\"\\nОсталось пропусков:\", int(prepared.isna().sum().sum()))\n", + "\n", + "X = prepared[feature_columns].copy()\n", + "y = prepared[target_column].copy()\n", + "print(\"Формы X, y:\", X.shape, y.shape)\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.3, stratify=y, random_state=42\n", + ")\n", + "print(\"X_train/X_test:\", X_train.shape, X_test.shape)\n", + "\n", + "for name, ser in [(\"y_train\", y_train), (\"y_test\", y_test)]:\n", + " vc = ser.value_counts(normalize=True).sort_index()\n", + " print(f\"\\nБаланс классов в {name}:\")\n", + " for cls, prop in vc.items():\n", + " print(f\" {cls}: {prop*100:.1f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "78a557cc", + "metadata": {}, + "source": [ + "# Блок 4. Кодирование категорий и скейлинг чисел\n", + "Используем `OneHotEncoder` для категориальных и `StandardScaler` для числовых; соберём всё в `ColumnTransformer`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b9214962", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Форма до трансформации: (22792, 14)\n", + "Форма после трансформации: (22792, 104)\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "from sklearn.compose import ColumnTransformer\n", + "\n", + "numeric_transformer = StandardScaler()\n", + "categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n", + "\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('scale_numeric', numeric_transformer, features_numeric),\n", + " ('onehot_categorical', categorical_transformer, features_categorical),\n", + " ],\n", + " remainder='drop'\n", + ")\n", + "\n", + "Xt = preprocessor.fit_transform(X_train)\n", + "print(\"Форма до трансформации:\", X_train.shape)\n", + "print(\"Форма после трансформации:\", Xt.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "5c075bd6", + "metadata": {}, + "source": [ + "# Блок 5. Обучаем Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cbcf886e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy (test): 0.849\n", + "F1-macro (test): 0.78\n", + "\n", + "Classification report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " <=50K 0.879 0.929 0.903 7417\n", + " >50K 0.727 0.597 0.656 2352\n", + "\n", + " accuracy 0.849 9769\n", + " macro avg 0.803 0.763 0.780 9769\n", + "weighted avg 0.843 0.849 0.844 9769\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score, f1_score, classification_report, ConfusionMatrixDisplay\n", + "\n", + "logreg_model = LogisticRegression(max_iter=1000, solver='lbfgs')\n", + "logreg_pipeline = Pipeline(steps=[('preprocess', preprocessor), ('clf', logreg_model)])\n", + "\n", + "logreg_pipeline.fit(X_train, y_train)\n", + "y_pred_lr = logreg_pipeline.predict(X_test)\n", + "\n", + "acc_lr = accuracy_score(y_test, y_pred_lr)\n", + "f1m_lr = f1_score(y_test, y_pred_lr, average='macro')\n", + "print(\"Accuracy (test):\", round(acc_lr, 3))\n", + "print(\"F1-macro (test):\", round(f1m_lr, 3))\n", + "\n", + "print(\"\\nClassification report:\\n\")\n", + "print(classification_report(y_test, y_pred_lr, digits=3))\n", + "\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_lr, cmap='Blues')\n", + "plt.title('Confusion Matrix — Logistic Regression')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d47f7096", + "metadata": {}, + "source": [ + "# Блок 6. Другая модель — KNN и RandomForest\n", + "Сравним результаты с KNN (а также попробуем посмотреть на RandomForest интереса ради)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5798e616", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== KNN (k=5) ===\n", + "Accuracy: 0.832 | F1-macro: 0.762\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== RandomForest ===\n", + "Accuracy: 0.85 | F1-macro: 0.784\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# KNN\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "knn_pipeline = Pipeline(steps=[('preprocess', preprocessor), ('clf', knn)])\n", + "knn_pipeline.fit(X_train, y_train)\n", + "y_pred_knn = knn_pipeline.predict(X_test)\n", + "acc_knn = accuracy_score(y_test, y_pred_knn)\n", + "f1m_knn = f1_score(y_test, y_pred_knn, average='macro')\n", + "print(\"=== KNN (k=5) ===\")\n", + "print(\"Accuracy:\", round(acc_knn, 3), \" | F1-macro:\", round(f1m_knn, 3))\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_knn, cmap='Purples')\n", + "plt.title('Confusion Matrix — KNN (k=5)')\n", + "plt.show()\n", + "\n", + "# RandomForest\n", + "rf = RandomForestClassifier(n_estimators=300, random_state=42)\n", + "rf_pipeline = Pipeline(steps=[('preprocess', preprocessor), ('clf', rf)])\n", + "rf_pipeline.fit(X_train, y_train)\n", + "y_pred_rf = rf_pipeline.predict(X_test)\n", + "acc_rf = accuracy_score(y_test, y_pred_rf)\n", + "f1m_rf = f1_score(y_test, y_pred_rf, average='macro')\n", + "print(\"\\n=== RandomForest ===\")\n", + "print(\"Accuracy:\", round(acc_rf, 3), \" | F1-macro:\", round(f1m_rf, 3))\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_rf, cmap='Greens')\n", + "plt.title('Confusion Matrix — RandomForest')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "05d703c7", + "metadata": {}, + "source": [ + "# Сводные результаты моделей" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f83fc6f8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Logistic Regression ===\n", + " precision recall f1-score support\n", + "\n", + " <=50K 0.879 0.929 0.903 7417\n", + " >50K 0.727 0.597 0.656 2352\n", + "\n", + " accuracy 0.849 9769\n", + " macro avg 0.803 0.763 0.780 9769\n", + "weighted avg 0.843 0.849 0.844 9769\n", + "\n", + "=== KNN (k=5) ===\n", + " precision recall f1-score support\n", + "\n", + " <=50K 0.877 0.905 0.891 7417\n", + " >50K 0.668 0.601 0.633 2352\n", + "\n", + " accuracy 0.832 9769\n", + " macro avg 0.773 0.753 0.762 9769\n", + "weighted avg 0.827 0.832 0.829 9769\n", + "\n", + "=== RandomForest ===\n", + " precision recall f1-score support\n", + "\n", + " <=50K 0.885 0.922 0.903 7417\n", + " >50K 0.716 0.622 0.666 2352\n", + "\n", + " accuracy 0.850 9769\n", + " macro avg 0.800 0.772 0.784 9769\n", + "weighted avg 0.844 0.850 0.846 9769\n", + "\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(18, 4))\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_lr, cmap='Blues', ax=axes[0])\n", + "axes[0].set_title('Logistic Regression')\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_knn, cmap='Purples', ax=axes[1])\n", + "axes[1].set_title('KNN (k=5)')\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_rf, cmap='Greens', ax=axes[2])\n", + "axes[2].set_title('RandomForest')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "from sklearn.metrics import classification_report\n", + "print('=== Logistic Regression ===')\n", + "print(classification_report(y_test, y_pred_lr, digits=3))\n", + "print('=== KNN (k=5) ===')\n", + "print(classification_report(y_test, y_pred_knn, digits=3))\n", + "print('=== RandomForest ===')\n", + "print(classification_report(y_test, y_pred_rf, digits=3))" + ] + }, + { + "cell_type": "markdown", + "id": "9dca0c1a", + "metadata": {}, + "source": [ + "- Лучше всего предсказывается класс большинства `<=50K` (выше recall и F1) у всех моделей. \n", + "- Класс `>50K` путается чаще: заметно ниже recall (много FN). \n", + "- У KNN выше доля FP для `>50K` (precision ниже), чем у логрега/RF. \n", + "- RandomForest, как правило, даёт лучший компромисс precision/recall для `>50K`, отсюда немного более высокий F1-macro." + ] + }, + { + "cell_type": "markdown", + "id": "6f748ff8", + "metadata": {}, + "source": [ + "# Блок 7. Эксперименты\n", + "Попробуем изменить параметры и посмотреть на метрики. Также добавим кросс-валидацию." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fd26e756", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k, Accuracy, F1-macro (KNN):\n", + "k=3: acc=0.824, f1m=0.752\n", + "k=5: acc=0.832, f1m=0.762\n", + "k=7: acc=0.840, f1m=0.772\n", + "k=9: acc=0.838, f1m=0.768\n", + "k=11: acc=0.840, f1m=0.772\n", + "\n", + "max_depth, Accuracy, F1-macro (RF):\n", + "max_depth=None: acc=0.850, f1m=0.784\n", + "max_depth=5: acc=0.839, f1m=0.733\n", + "max_depth=10: acc=0.856, f1m=0.778\n", + "max_depth=15: acc=0.858, f1m=0.784\n", + "max_depth=20: acc=0.859, f1m=0.790\n" + ] + } + ], + "source": [ + "# KNN по k\n", + "results_knn = []\n", + "for k in [3, 5, 7, 9, 11]:\n", + " model = KNeighborsClassifier(n_neighbors=k)\n", + " pipe = Pipeline(steps=[('preprocess', preprocessor), ('clf', model)])\n", + " pipe.fit(X_train, y_train)\n", + " pred = pipe.predict(X_test)\n", + " results_knn.append((k, accuracy_score(y_test, pred), f1_score(y_test, pred, average='macro')))\n", + "\n", + "print(\"k, Accuracy, F1-macro (KNN):\")\n", + "for k, a, f in results_knn:\n", + " print(f\"k={k}: acc={a:.3f}, f1m={f:.3f}\")\n", + "\n", + "# RandomForest по max_depth\n", + "results_rf = []\n", + "for md in [None, 5, 10, 15, 20]:\n", + " model = RandomForestClassifier(n_estimators=300, max_depth=md, random_state=42)\n", + " pipe = Pipeline(steps=[('preprocess', preprocessor), ('clf', model)])\n", + " pipe.fit(X_train, y_train)\n", + " pred = pipe.predict(X_test)\n", + " results_rf.append((md, accuracy_score(y_test, pred), f1_score(y_test, pred, average='macro')))\n", + "\n", + "print(\"\\nmax_depth, Accuracy, F1-macro (RF):\")\n", + "for md, a, f in results_rf:\n", + " print(f\"max_depth={md}: acc={a:.3f}, f1m={f:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4dc74f43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== LinearRegression (threshold=0.5) ===\n", + "Accuracy: 0.838 | F1-macro: 0.749\n", + "Classification report:\n", + " precision recall f1-score support\n", + "\n", + " <=50K 0.857 0.945 0.899 7417\n", + " >50K 0.743 0.503 0.600 2352\n", + "\n", + " accuracy 0.838 9769\n", + " macro avg 0.800 0.724 0.749 9769\n", + "weighted avg 0.829 0.838 0.827 9769\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# LinearRegression как классификатор (по порогу 0.5)\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# Определим положительный класс как не-м Majority\n", + "majority = y_train.mode().iloc[0]\n", + "classes_all = sorted(y.unique().tolist())\n", + "pos_label = [c for c in classes_all if c != majority][0]\n", + "y_train_bin = (y_train == pos_label).astype(int)\n", + "y_test_bin = (y_test == pos_label).astype(int)\n", + "\n", + "linreg = LinearRegression()\n", + "linreg_pipe = Pipeline(steps=[('preprocess', preprocessor), ('reg', linreg)])\n", + "linreg_pipe.fit(X_train, y_train_bin)\n", + "y_pred_reg_prob = linreg_pipe.predict(X_test)\n", + "y_pred_reg_prob = np.clip(y_pred_reg_prob, 0, 1)\n", + "y_pred_reg_bin = (y_pred_reg_prob >= 0.5).astype(int)\n", + "y_pred_reg_lbl = np.where(y_pred_reg_bin == 1, pos_label, majority)\n", + "\n", + "acc_lin = accuracy_score(y_test, y_pred_reg_lbl)\n", + "f1m_lin = f1_score(y_test, y_pred_reg_lbl, average='macro')\n", + "print('=== LinearRegression (threshold=0.5) ===')\n", + "print('Accuracy:', round(acc_lin, 3), '| F1-macro:', round(f1m_lin, 3))\n", + "from sklearn.metrics import classification_report, ConfusionMatrixDisplay\n", + "print('Classification report:')\n", + "print(classification_report(y_test, y_pred_reg_lbl, digits=3))\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred_reg_lbl, cmap='Reds')\n", + "plt.title('Confusion Matrix — LinearRegression (thr=0.5)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fc07b098", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogReg CV Accuracy: mean=0.851 ± 0.004\n", + "LogReg CV F1-macro: mean=0.782 ± 0.005\n" + ] + } + ], + "source": [ + "# Кросс-валидация\n", + "from sklearn.model_selection import cross_val_score, StratifiedKFold\n", + "\n", + "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", + "cv_scores_acc = cross_val_score(logreg_pipeline, X, y, cv=cv, scoring='accuracy')\n", + "cv_scores_f1m = cross_val_score(logreg_pipeline, X, y, cv=cv, scoring='f1_macro')\n", + "print(f\"LogReg CV Accuracy: mean={cv_scores_acc.mean():.3f} ± {cv_scores_acc.std():.3f}\")\n", + "print(f\"LogReg CV F1-macro: mean={cv_scores_f1m.mean():.3f} ± {cv_scores_f1m.std():.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "325d1c09", + "metadata": {}, + "source": [ + "# Итог\n", + "\n", + "- Какая модель лучше работает: \n", + " - По тесту лучший результат дал **RandomForest**: Accuracy ≈ 0.850, F1-macro ≈ 0.784; при подборе `max_depth=20` улучшение до Accuracy ≈ 0.859 и F1-macro ≈ 0.790. \n", + " - **Logistic Regression** очень близко (Accuracy ≈ 0.849, F1-macro ≈ 0.780) и служит сильным интерпретируемым бейзлайном. \n", + " - **KNN (k=5)** хуже по обоим метрикам (Accuracy ≈ 0.832, F1-macro ≈ 0.762); при k=7/11 F1-macro ≈ 0.772, но всё ещё ниже RF/LogReg. \n", + " - **LinearRegression** как классификатор (порог 0.5) — самая слабая из рассмотренных: Accuracy ≈ 0.838, F1-macro ≈ 0.749. \n", + "\n", + "- Какие признаки важны: \n", + " - Для Adult традиционно наиболее информативны: `capital.gain`, `capital.loss`, `education.num`, `age`, `hours.per.week`, а также части категориальных признаков, связанных с семейным положением (`marital.status`) и занятостью (`occupation`, `workclass`). \n", + " - Это согласуется с поведением моделей: существенное разделение классов обеспечивают признаки доходов/капитала и образования, тогда как чисто категориальные в OHE-формате дают дополнительный, но более слабый вклад. \n", + "\n", + "- Какие ошибки чаще всего: \n", + " - Все модели хуже распознают класс `>50K`: recall около 0.60, много **ложных отрицаний**. \n", + " - У **KNN** заметно больше **ложных срабатываний** на `>50K`, чем у LogReg/RF. \n", + " - **RandomForest** даёт лучший компромисс precision/recall для `>50K`, отсюда немного более высокий F1-macro. \n", + "\n", + "- Кросс-валидация (доп. балл): \n", + " - Для **Logistic Regression**: CV Accuracy ≈ 0.851 ± 0.004, CV F1-macro ≈ 0.782 ± 0.005 (5-fold Stratified). \n", + " - Расхождение небольшое — качество стабильно между фолдами, что подтверждает корректность препроцессинга и разбиения. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}