本文将介绍基于sklearn实现MLP（多层感知机）算法的过程。
1.读取训练数据
mldata_x = data[['学科A','学科B','学科C','学科D']]
mldata_y = data[['学业成败']]
2.划分训练数据集和测试数据集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(mldata_x, mldata_y, test_size = 0.2, random_state = 10)
3.模型训练与评价
from sklearn.neural_network import MLPClassifier
mp= MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5,2), random_state=1)
mp.fit(X_train, y_train)
MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(5, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)
Predict = mp.predict(X_test)
mp_Score = accuracy_score(y_test,Predict)
mp_Score
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