Завершил задание

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2025-05-14 17:09:02 +03:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "784f3b45-efc8-49dc-b677-cc994b66d7d4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 8\n",
" 1 0.73 1.00 0.84 8\n",
" 2 1.00 0.79 0.88 14\n",
"\n",
" accuracy 0.90 30\n",
" macro avg 0.91 0.93 0.91 30\n",
"weighted avg 0.93 0.90 0.90 30\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Files\\4week\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.metrics import classification_report\n",
"\n",
"# Загрузка и разбиение данных\n",
"X, y = load_iris(return_X_y=True)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
"\n",
"# Модель MLP — многослойный перцептрон\n",
"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=500)\n",
"clf.fit(X_train, y_train)\n",
"\n",
"# Отчёт о точности\n",
"print(classification_report(y_test, clf.predict(X_test)))"
]
},
{
"cell_type": "markdown",
"id": "945fb3ca-7876-49ef-9418-0486618ac07b",
"metadata": {},
"source": [
"Выполнил код, данный в примере"
]
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.3"
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