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1e252c4c5f
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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.ipynb_checkpoints/week4_scikit_learn-checkpoint.ipynb
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.ipynb_checkpoints/week4_scikit_learn-checkpoint.ipynb
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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1
Boston-house-price-data.csv
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1
Boston-house-price-data.csv
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784
week4_feature_selection.ipynb
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week4_feature_selection.ipynb
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week4_scikit_learn.ipynb
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week4_scikit_learn.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "7f8f4a9c-fddd-483a-b048-9afd30fb3bc2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==================================================\n",
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"MLPClassifier\n",
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"==================================================\n",
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"\n",
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"1. max_iter = 100 (недообучение)\n",
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" Точность на тесте: 0.4000\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 10\n",
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" 1 0.00 0.00 0.00 9\n",
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" 2 0.18 0.18 0.18 11\n",
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"\n",
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" accuracy 0.40 30\n",
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" macro avg 0.39 0.39 0.39 30\n",
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"weighted avg 0.40 0.40 0.40 30\n",
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"\n",
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"\n",
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"2. max_iter = 500 (оптимальное)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Пользователь\\PycharmProjects\\PythonProject7\\week4-feature-selection\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
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" warnings.warn(\n",
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"C:\\Users\\Пользователь\\PycharmProjects\\PythonProject7\\week4-feature-selection\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Точность на тесте: 0.9333\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 10\n",
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" 1 1.00 0.78 0.88 9\n",
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" 2 0.85 1.00 0.92 11\n",
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"\n",
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" accuracy 0.93 30\n",
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" macro avg 0.95 0.93 0.93 30\n",
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"weighted avg 0.94 0.93 0.93 30\n",
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"\n",
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"\n",
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"3. max_iter = 2500 (долгое обучение)\n",
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" Точность на тесте: 0.9667\n",
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 10\n",
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" 1 1.00 0.89 0.94 9\n",
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" 2 0.92 1.00 0.96 11\n",
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"\n",
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" accuracy 0.97 30\n",
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" macro avg 0.97 0.96 0.97 30\n",
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"weighted avg 0.97 0.97 0.97 30\n",
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"\n",
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"\n",
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"==================================================\n",
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"ВЫВОДЫ\n",
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"==================================================\n",
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"max_iter=100 → точность: 0.4000 (недообучение)\n",
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"max_iter=500 → точность: 0.9333 (оптимально)\n",
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"max_iter=2500 → точность: 0.9667 (могло переобучиться)\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"import numpy as np\n",
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"\n",
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"# Загрузка и разбиение данных\n",
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"X, y = load_iris(return_X_y=True)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"\n",
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"print(\"=\"*50)\n",
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"print(\"MLPClassifier\")\n",
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"print(\"=\"*50)\n",
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"\n",
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"# max_iter = 100 (недообучение)\n",
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"print(\"\\n1. max_iter = 100 (недообучение)\")\n",
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"clf_100 = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=100, random_state=42)\n",
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"clf_100.fit(X_train, y_train)\n",
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"print(f\" Точность на тесте: {clf_100.score(X_test, y_test):.4f}\")\n",
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"print(classification_report(y_test, clf_100.predict(X_test), zero_division=0))\n",
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"\n",
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"# max_iter = 500 (оптимальное)\n",
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"print(\"\\n2. max_iter = 500 (оптимальное)\")\n",
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"clf_500 = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=500, random_state=42)\n",
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"clf_500.fit(X_train, y_train)\n",
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"print(f\" Точность на тесте: {clf_500.score(X_test, y_test):.4f}\")\n",
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"print(classification_report(y_test, clf_500.predict(X_test)))\n",
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"\n",
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"# max_iter = 2500 (переобучение)\n",
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"print(\"\\n3. max_iter = 2500 (долгое обучение)\")\n",
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"clf_2500 = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, random_state=42)\n",
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"clf_2500.fit(X_train, y_train)\n",
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"print(f\" Точность на тесте: {clf_2500.score(X_test, y_test):.4f}\")\n",
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"print(classification_report(y_test, clf_2500.predict(X_test)))\n",
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"\n",
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"# Вывод\n",
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"print(\"\\n\" + \"=\"*50)\n",
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"print(\"ВЫВОДЫ\")\n",
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"print(\"=\"*50)\n",
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"print(f\"max_iter=100 → точность: {clf_100.score(X_test, y_test):.4f} (недообучение)\")\n",
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"print(f\"max_iter=500 → точность: {clf_500.score(X_test, y_test):.4f} (оптимально)\")\n",
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"print(f\"max_iter=2500 → точность: {clf_2500.score(X_test, y_test):.4f} (могло переобучиться)\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "555f6188-4462-4b9c-b17d-fa0788a7e3eb",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5ccb37ef-fb03-406c-8017-d0b74a00ea38",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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