import matplotlib.pyplot as plt import mglearn from scipy import sparse import numpy as np import matplotlib as mt import pandas as pd from IPython.display import display from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split #wave数据集 #wave数据集只有一个特征 #公式为y=w[0]x[0]+b #w为斜率，b为轴偏移或截距，分别在sklearn中使用 coef_[0], intercept_表示 mglearn.plots.plot_linear_regression_wave() plt.show() #boston数据集 #boston数据集有506个样本，105个特征 X, y = mglearn.datasets.load_extended_boston() X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) 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)))
结果：
w[0]: 0.393906 b: -0.031804
plot_linear_regression_wave源码
import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from .datasets import make_wave from .plot_helpers import cm2 def plot_linear_regression_wave(): X, y = make_wave(n_samples=60) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) line = np.linspace(-3, 3, 100).reshape(-1, 1) lr = LinearRegression().fit(X_train, y_train) print("w[0]: %f b: %f" % (lr.coef_[0], lr.intercept_)) plt.figure(figsize=(8, 8)) plt.plot(line, lr.predict(line)) plt.plot(X, y, 'o', c=cm2(0)) ax = plt.gca() ax.spines['left'].set_position('center') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position('center') ax.spines['top'].set_color('none') ax.set_ylim(-3, 3) #ax.set_xlabel("Feature") #ax.set_ylabel("Target") ax.legend(["model", "training data"], loc="best") ax.grid(True) ax.set_aspect('equal')
结果2：
Training set score: 0.95
Test set score: 0.61
可以看出出现了过拟合，这是因为波士顿房价的各个特征的差距非常大，不适合使用最小二乘法，需要使用“正则化”来做显式约束，使用岭回归避免过拟合。
Ridge岭回归用到L2正则化。
Lasso回归用到L1正则，还可以使用ElasticNet。
来源：博客园
作者：昕友软件开发
链接：https://www.cnblogs.com/starcrm/p/11676633.html