代码：
1 # -*- coding: utf-8 -*- 2 """ 3 Created on Mon Jul 16 09:08:09 2018 4 5 @author: zhen 6 """ 7 8 from sklearn.linear_model import LinearRegression, Ridge, Lasso 9 import mglearn 10 from sklearn.model_selection import train_test_split 11 import matplotlib.pyplot as plt 12 import numpy as np 13 # 线性回归 14 x, y = mglearn.datasets.load_extended_boston() 15 x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0) 16 17 linear_reg = LinearRegression() 18 lr = linear_reg.fit(x_train, y_train) 19 20 print("lr.coef_:{}".format(lr.coef_)) # 斜率 21 print("lr.intercept_:{}".format(lr.intercept_)) # 截距 22 23 print("="*25+"线性回归"+"="*25) 24 print("Training set score:{:.2f}".format(lr.score(x_train, y_train))) 25 print("Rest set score:{:.2f}".format(lr.score(x_test, y_test))) 26 27 """ 28 总结： 29 训练集和测试集上的分数非常接近，这说明可能存在欠耦合。 30 训练集和测试集之间的显著性能差异是过拟合的明显标志。解决方式是使用岭回归！ 31 """ 32 print("="*25+"岭回归（默认值1.0）"+"="*25) 33 # 岭回归 34 ridge = Ridge().fit(x_train, y_train) 35 36 print("Training set score:{:.2f}".format(ridge.score(x_train, y_train))) 37 print("Test set score:{:.2f}".format(ridge.score(x_test, y_test))) 38 39 print("="*25+"岭回归（alpha=10）"+"="*25) 40 # 岭回归 41 ridge_10 = Ridge(alpha=10).fit(x_train, y_train) 42 43 print("Training set score:{:.2f}".format(ridge_10.score(x_train, y_train))) 44 print("Test set score:{:.2f}".format(ridge_10.score(x_test, y_test))) 45 46 print("="*25+"岭回归（alpha=0.1）"+"="*25) 47 # 岭回归 48 ridge_01 = Ridge(alpha=0.1).fit(x_train, y_train) 49 50 print("Training set score:{:.2f}".format(ridge_01.score(x_train, y_train))) 51 print("Test set score:{:.2f}".format(ridge_01.score(x_test, y_test))) 52 53 54 # 可视化 55 fig = plt.figure(10) 56 plt.subplots_adjust(wspace =0, hspace =0.6)#调整子图间距 57 ax1 = plt.subplot(2, 1, 1) 58 59 ax2 = plt.subplot(2, 1, 2) 60 61 ax1.plot(ridge_01.coef_, 'v', label="Ridge alpha=0.1") 62 ax1.plot(ridge.coef_, 's', label="Ridge alpha=1") 63 ax1.plot(ridge_10.coef_, '^', label="Ridge alpha=10") 64 65 ax1.plot(lr.coef_, 'o', label="LinearRegression") 66 67 68 ax1.set_ylabel("Cofficient magnitude") 69 ax1.set_ylim(-25,25) 70 ax1.hlines(0, 0, len(lr.coef_)) 71 ax1.legend(ncol=2, loc=(0.1, 1.05)) 72 73 print("="*25+"Lasso回归（默认配置）"+"="*25) 74 lasso = Lasso().fit(x_train, y_train) 75 76 print("Training set score:{:.2f}".format(lasso.score(x_train, y_train))) 77 print("Test set score:{:.2f}".format(lasso.score(x_test, y_test))) 78 print("Number of features used:{}".format(np.sum(lasso.coef_ != 0))) 79 80 print("="*25+"Lasso回归（aplpha=0.01）"+"="*25) 81 lasso_001 = Lasso(alpha=0.01, max_iter=1000).fit(x_train, y_train) 82 83 print("Training set score:{:.2f}".format(lasso_001.score(x_train, y_train))) 84 print("Test set score:{:.2f}".format(lasso_001.score(x_test, y_test))) 85 print("Number of features used:{}".format(np.sum(lasso_001.coef_ != 0))) 86 87 88 print("="*15+"Lasso回归（aplpha=0.0001）太小可能会过拟合"+"="*15) 89 lasso_00001 = Lasso(alpha=0.0001, max_iter=1000).fit(x_train, y_train) 90 91 print("Training set score:{:.2f}".format(lasso_00001.score(x_train, y_train))) 92 print("Test set score:{:.2f}".format(lasso_00001.score(x_test, y_test))) 93 print("Number of features used:{}".format(np.sum(lasso_00001.coef_ != 0))) 94 95 96 # 可视化 97 ax2.plot(ridge_01.coef_, 'o', label="Ridge alpha=0.1") 98 ax2.plot(lasso.coef_, 's', label="lasso alpha=1") 99 ax2.plot(lasso_001.coef_, '^', label="lasso alpha=0.001")100 ax2.plot(lasso_00001.coef_, 'v', label="lasso alpha=0.00001")101 102 ax2.set_ylabel("Cofficient magnitude")103 ax2.set_xlabel("Coefficient index")104 ax2.set_ylim(-25,25)105 ax2.legend(ncol=2, loc=(0.1, 1)) 结果：
总结：各回归算法在相同的测试数据中表现差距很多，且算法内的配置参数调整对自身算法的效果影响也是巨大的，
因此合理挑选合适的算法和配置合适的配置参数是使用算法的关键！