多分类概率预测

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
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)
clf = MLPClassifier(hidden_layer_sizes=(10,), max_iter=500, random_state=42)
clf.fit(X_train, y_train)
proba = clf.predict_proba(X_test[:3])
print('前3个样本预测概率:')
for i, p in enumerate(proba):
    print(f'样本{i+1}: 概率和={sum(p):.6f}')