不同随机种子的影响

from sklearn.datasets import load_iris
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import numpy as np
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
accs = []
for seed in [0, 42, 100]:
    clf = MLPClassifier(hidden_layer_sizes=(10,5), max_iter=500, random_state=seed)
    clf.fit(X_train, y_train)
    acc = accuracy_score(y_test, clf.predict(X_test))
    accs.append(acc)
    print(f'random_state={seed}: 准确率={acc:.4f}')
print(f'平均: {np.mean(accs):.4f}')