Python机器学习：多项式回归与模型泛化009LASSO回归

代码接着008

Lasso

#LASSOfrom sklearn.linear_model import Lassodef LassoRegression(degree,alpha):return Pipeline([('poly',PolynomialFeatures(degree=degree)),('std_scaler',StandardScaler()),('lasso_reg',Lasso(alpha = alpha))])lasso1_reg = LassoRegression(20,0.01)lasso1_reg.fit(X_train,y_train)y1_predict = lasso1_reg.predict(X_test)print(mean_squared_error(y_test,y1_predict))

1.149608084325997

plot_model(lasso1_reg)

alpha = 0.1

#LASSOfrom sklearn.linear_model import Lassodef LassoRegression(degree,alpha):return Pipeline([('poly',PolynomialFeatures(degree=degree)),('std_scaler',StandardScaler()),('lasso_reg',Lasso(alpha = alpha))])lasso1_reg = LassoRegression(20,0.1)lasso1_reg.fit(X_train,y_train)y1_predict = lasso1_reg.predict(X_test)print(mean_squared_error(y_test,y1_predict))

1.1213911351818648

plot_model(lasso1_reg)

alpha = 1

#LASSOfrom sklearn.linear_model import Lassodef LassoRegression(degree,alpha):return Pipeline([('poly',PolynomialFeatures(degree=degree)),('std_scaler',StandardScaler()),('lasso_reg',Lasso(alpha = alpha))])lasso1_reg = LassoRegression(20,1)lasso1_reg.fit(X_train,y_train)y1_predict = lasso1_reg.predict(X_test)print(mean_squared_error(y_test,y1_predict))plot_model(lasso1_reg)

1.8408939659515595

Selection operator

使用ridge很难得到直线，lasso特性（特征选择，让一些参数为0而不是整体最小。。。）