#划分train from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=1) #构造Ridge的模型 from sklearn import linear_model lassoRegression = linear_model.Ridge() lassoRegression.fit(X_train, y_train) print("权重向量:%s, b的值为:%.2f" % (lassoRegression.coef_, lassoRegression.intercept_)) print("损失函数的值:%.2f" % np.mean((lassoRegression.predict(X_test) - y_test) ** 2)) print("预测性能得分: %.2f" % lassoRegression.score(X_test, y_test)) #构造Lasso的模型 ridgeRegression = linear_model.Lasso() ridgeRegression.fit(X_train, y_train) print("权重向量:%s, b的值为:%.2f" % (ridgeRegression.coef_, ridgeRegression.intercept_)) print("损失函数的值:%.2f" % np.mean((ridgeRegression.predict(X_test) - y_test) ** 2)) print("预测性能得分: %.2f" % ridgeRegression.score(X_test, y_test)) #Lasso预测 y_pred = lassoRegression.predict(X_test) #Ridge预测 y_pred = ridgeRegression.predict(X_test) #mean_squared_error值 msn = np.sqrt(mean_squared_error(y_test, y_pred)) mean_squared_error越小越好