pycharm中机器学习练习.zip

# 线性回归三种：普通线性回归、岭回归、lasso回归 import numpy as np import mglearn import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.linear_model import Ridge from sklearn.linear_model import Lasso # 获取数据 X, y = mglearn.datasets.load_extended_boston() # 将数据分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) print("普通线性回归") # 普通线性回归算法（无正则化） lr = LinearRegression().fit(X_train, y_train) print("Training set score: {:.2f}".format(lr.score(X_train, y_train))) print("Test set score: {:.2f}".format(lr.score(X_test, y_test))) print("岭回归") # 岭回归算法（普通线性加入正则化L2，正则化参数默认为1） ridge = Ridge().fit(X_train, y_train) print("Training set score: {:.2f}".format(ridge.score(X_train, y_train))) print("Test set score: {:.2f}".format(ridge.score(X_test, y_test))) # 将正则化参数设为10，减少拟合能力，增加泛化能力（提高偏差，降低方差） ridge10 = Ridge(alpha=10).fit(X_train, y_train) print("Training set score: {:.2f}".format(ridge10.score(X_train, y_train))) print("Test set score: {:.2f}".format(ridge10.score(X_test, y_test))) # 将正则化参数设为0.1，减少泛化能力，增加拟合能力（提高方差，降低偏差） ridge01 = Ridge(alpha=0.1).fit(X_train, y_train) print("Training set score: {:.2f}".format(ridge01.score(X_train, y_train))) print("Test set score: {:.2f}".format(ridge01.score(X_test, y_test))) print("lasso回归") # lasso回归算法（普通线性加入正则化L1，正则化参数默认为1，会自动忽略一些不重要的特征） lasso = Lasso().fit(X_train, y_train) print("Training set score: {:.2f}".format(lasso.score(X_train, y_train))) print("Test set score: {:.2f}".format(lasso.score(X_test, y_test))) print("Number of features used:", np.sum(lasso.coef_ != 0)) # 将正则化参数设为0.01，参数降低，特征值数量保留变多。减少泛化能力，增加拟合能力（提高方差，降低偏差） # "max_iter"运行迭代的最大次数 # otherwise the model would warn us that we should increase max_iter. lasso001 = Lasso(alpha=0.01, max_iter=100000).fit(X_train, y_train) print("Training set score: {:.2f}".format(lasso001.score(X_train, y_train))) print("Test set score: {:.2f}".format(lasso001.score(X_test, y_test))) print("Number of features used:", np.sum(lasso001.coef_ != 0)) lasso00001 = Lasso(alpha=0.0001, max_iter=100000).fit(X_train, y_train) print("Training set score: {:.2f}".format(lasso00001.score(X_train, y_train))) print("Test set score: {:.2f}".format(lasso00001.score(X_test, y_test))) print("Number of features used:", np.sum(lasso00001.coef_ != 0)) plt.plot(ridge.coef_, 's', label="Ridge alpha=1") plt.plot(ridge10.coef_, '^', label="Ridge alpha=10") plt.plot(ridge01.coef_, 'v', label="Ridge alpha=0.1") plt.plot(lr.coef_, 'o', label="LinearRegression") plt.xlabel("Coefficient index") plt.ylabel("Coefficient magnitude") # 获取当前x轴左，右数值 xlims = plt.xlim() # 画一条横线第一个参数y坐标，第二个x起点，第三个x终点 plt.hlines(0, xlims[0], xlims[1]) plt.xlim(xlims) plt.ylim(-25, 25) # 自动获取签名 plt.legend() plt.show() # 这个学习曲线展示 mglearn.plots.plot_ridge_n_samples() plt.show() plt.plot(lasso.coef_, 's', label="Lasso alpha=1") plt.plot(lasso001.coef_, '^', label="Lasso alpha=0.01") plt.plot(lasso00001.coef_, 'v', label="Lasso alpha=0.0001") plt.plot(ridge01.coef_, 'o', label="Ridge alpha=0.1") # ncol表示图例展示列数，loc表示图例显示位置 # X,y用于定位图例，也可用单键词"bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center"代替 plt.legend(ncol=2, loc=(0, 1.05)) plt.ylim(-25, 25) plt.xlabel("Coefficient index") plt.ylabel("Coefficient magnitude") plt.show()