87 lines
3.3 KiB
Plaintext
87 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "df20729e-7575-4880-a900-46b2b8bd82e8",
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"text": [
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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 1.00 1.00 9\n",
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" 2 1.00 1.00 1.00 11\n",
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"\n",
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" accuracy 1.00 30\n",
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" macro avg 1.00 1.00 1.00 30\n",
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"weighted avg 1.00 1.00 1.00 30\n",
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"\n"
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]
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}
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],
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"source": [
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"# Импорт необходимых модулей\n",
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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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"\n",
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"# 1. Загрузка данных\n",
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"# load_iris() загружает встроенный набор данных Iris (ирисы Фишера)\n",
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"# return_X_y=True возвращает данные в виде матрицы признаков (X) и вектора меток (y)\n",
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"X, y = load_iris(return_X_y=True)\n",
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"\n",
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"# 2. Разделение данных на обучающую и тестовую выборки\n",
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"# test_size=0.2 означает, что 20% данных пойдут в тестовую выборку\n",
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"# random_state можно задать для воспроизводимости результатов\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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"# 3. Создание и обучение модели нейронной сети\n",
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"# MLPClassifier - многослойный перцептрон\n",
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"# hidden_layer_sizes=(10,) - один скрытый слой с 10 нейронами\n",
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"# activation='relu' - функция активации Rectified Linear Unit\n",
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"# max_iter=500 - максимальное количество итераций обучения\n",
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"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=10000)\n",
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"clf.fit(X_train, y_train) # Обучение модели на тренировочных данных\n",
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"\n",
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"# 4. Оценка модели\n",
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"# classification_report выводит отчет с основными метриками качества\n",
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"print(classification_report(y_test, clf.predict(X_test)))"
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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": "af5eae95-22ed-46c6-8f3e-befb67a2d9e6",
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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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"file_extension": ".py",
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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": "3.11.9"
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