Машинное обучение — Scikit-learn

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_breast_cancer

# Загружаем данные
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.3)

# Создаем конвейер с поиском гиперпараметров
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier())
])

param_grid = {
    'classifier__n_estimators': [50, 100, 200],
    'classifier__max_depth': [5, 10, None]
}

grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)

print(f"Лучшие параметры: {grid_search.best_params_}")
print(f"Лучшая точность: {grid_search.best_score_:.4f}")
print(f"Точность на тесте: {grid_search.score(X_test, y_test):.4f}")