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from sklearn.datasets import load_bostonboston = load_boston()boston.keys()print(boston.data) |
2. 一元线性回归模型，建立一个变量与房价之间的预测模型，并图形化显示。
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import pandas as pd #导包pd.DataFrame(boston.data) #预处理获取斜率 from sklearn.linear_model import LinearRegressionLineR = LinearRegression()LineR.fit(x.reshape(-1,1),y)w=LineR.coef_ #图形化显示x = data[:,5]y = boston.target import matplotlib.pyplot as pltplt.scatter(x,y)plt.plot(x,w*x+b,‘G‘)plt.show() |
3. 多元线性回归模型，建立13个变量与房价之间的预测模型，并检测模型好坏，并图形化显示检查结果。
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from sklearn.linear_model import LinearRegressionlineR = LinearRegression()lineR.fit(boston.data,y)w = lineR.coef_b = lineR.intercept_ import matplotlib.pyplot as pltx=boston.data[:,12].reshape(-1,1)y=boston.targetplt.figure(figsize=(10,6)) #指定显示图大小plt.scatter(x,y) from sklearn.linear_model import LinearRegressionlineR=LinearRegression()lineR.fit(x,y)y_pred=lineR.predict(x)plt.plot(x,y_pred,‘G‘)print(lineR.coef_,lineR.intercept_)plt.show() |
4. 一元多项式回归模型，建立一个变量与房价之间的预测模型，并图形化显示。
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xx = data[:,12].reshape(-1,1)plt.scatter(xx,y)plt.show() lr12 = LinearRegression()lr12.fit(xx,y)w = lr12.coef_b = lr12.intercept_plt.scatter(xx,y)plt.plot(xx,w*xx+b,‘G‘)plt.show() from sklearn.preprocessing import PolynomialFeaturesp = PolynomialFeatures()p.fit(xx)x_poly = p.transform(xx) lrp = LinearRegression()lrp.fit(x_poly,y)lrp.coef_lrp.intercept_ lrp = LinearRegression()lrp.fit(x_poly,y)y_poly = lrp.predict(x_poly)plt.scatter(xx,y)plt.plot(xx,w*xx+b,‘G‘)plt.scatter(xx,y_poly)plt.show()lrp.coef_ |
原文：https://www.cnblogs.com/yulinzzz/p/10134151.html