Wine数据集的GridSearch基础

from sklearn.datasets import load_wine
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
wine = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
param_grid = {'hidden_layer_sizes': [(10,), (20,), (10,5), (20,10)]}
clf = MLPClassifier(max_iter=500, random_state=42)
grid = GridSearchCV(clf, param_grid, cv=3)
grid.fit(X_train, y_train)
print(f'最佳参数: {grid.best_params_}')