分別使用1次,2次,4次多項式迴歸模型在比薩訓練樣本上進行擬合
阿新 • • 發佈:2019-01-19
import matplotlib.pyplot as plt x_train = [[6], [8], [10], [14], [18]] y_train = [[7], [9], [13], [17.5], [18]] from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(x_train, y_train) import numpy as np xx = np.linspace(0, 26, 100)#生成了1行100列的一個矩陣 #print(xx) xx = xx.reshape(xx.shape[0], 1)#將1行100列的矩陣轉化成100行1列的矩陣形式 #print(xx) yy = regressor.predict(xx) from sklearn.preprocessing import PolynomialFeatures #初始化二次多項式生成器 poly2 = PolynomialFeatures(degree=2) x_train_poly2 = poly2.fit_transform(x_train) regressor_poly2 = LinearRegression() regressor_poly2.fit(x_train_poly2, y_train) xx_poly2 = poly2.transform(xx) yy_poly2 = regressor_poly2.predict(xx_poly2) #初始化四次多項式生成器 poly4 = PolynomialFeatures(degree=4) x_train_poly4 = poly4.fit_transform(x_train) regressor_poly4 = LinearRegression() #對四次多項式迴歸模型進行訓練 regressor_poly4.fit(x_train_poly4, y_train) xx_poly4 = poly4.transform(xx) yy_poly4 = regressor_poly4.predict(xx_poly4) plt.scatter(x_train, y_train) plt1, = plt.plot(xx, yy, label='Degree=1') plt2, = plt.plot(xx, yy_poly2, label='Degree=2') plt4, = plt.plot(xx, yy_poly4, label='Degree=4') plt.axis([0, 25, 0, 25]) plt.xlabel('Diameter of Pizza') plt.ylabel('Pirce of Pizza') plt.legend(handles=[plt1, plt2, plt4]) plt.show() #評估3種迴歸模型在訓練集上的R-squared值 print('The R-squared value of Polynomonal Regressor(Degree=1) performing on the training data is', regressor.score(x_train, y_train)) print('The R-squared value of Polynomonal Regressor(Degree=2) performing on the training data is', regressor_poly2.score(x_train_poly2, y_train)) print('The R-squared value of Polynomonal Regressor(Degree=4) performing on the training data is', regressor_poly4.score(x_train_poly4, y_train)) #評估3種迴歸模型在測試資料集上的效能表現 x_test = [[6], [8], [11], [16]] y_test = [[8], [12], [15], [18]] print("線性模型效能評估:",regressor.score(x_test, y_test)) x_test_poly2 = poly2.transform(x_test) print("2次多項式迴歸模型的效能評估:",regressor_poly2.score(x_test_poly2, y_test)) x_test_poly4 = poly4.transform(x_test) print("4次多項式迴歸模型的效能評估:",regressor_poly4.score(x_test_poly4, y_test))
執行結果:
最終效果圖:The R-squared value of Polynomonal Regressor(Degree=1) performing on the training data is 0.910001596424 The R-squared value of Polynomonal Regressor(Degree=2) performing on the training data is 0.98164216396 The R-squared value of Polynomonal Regressor(Degree=4) performing on the training data is 1.0 線性模型效能評估: 0.809726797708 2次多項式迴歸模型的效能評估: 0.867544365635 4次多項式迴歸模型的效能評估: 0.809588079578