线性回归和岭回归
? 我近半年每個(gè)月所寫博客的數(shù)量
# -*- coding: utf-8 -*- """ Created on Fri Sep 1 18:23:07 2017@author: Administrator """ from sklearn import linear_model import numpy as np import matplotlib.pyplot as plt y=np.array([13,12,32,0,1,7,27]).reshape(-1,1) x=np.array([2,3,4,5,6,7,8]).reshape(-1,1)plt.plot(x,y)plt.rcParams['font.sans-serif'] = ['SimHei'] #用來正常顯示中文標(biāo)簽 plt.rcParams['axes.unicode_minus'] = False #用來正常顯示負(fù)號(hào)##設(shè)置模型 model = linear_model.LinearRegression() ##訓(xùn)練數(shù)據(jù) model.fit(x, y) ##用訓(xùn)練得出的模型預(yù)測(cè)數(shù)據(jù) y_plot = model.predict(x) ##打印線性方程的權(quán)重 print(model.coef_) ## 0.90045842、plt.scatter(x, y, color='red',label="樣本數(shù)據(jù)",linewidth=2) plt.plot(x, y_plot, color='green',label="擬合直線",linewidth=2) plt.legend(loc='lower right') plt.show()from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import Ridge##這里指定使用嶺回歸作為基函數(shù) model = make_pipeline(PolynomialFeatures(15), Ridge()) model.fit(x, y) ##根據(jù)模型預(yù)測(cè)結(jié)果 y_plot = model.predict(x)##繪圖 plt.scatter(x, y, color='red',label="樣本數(shù)據(jù)",linewidth=2) plt.plot(x, y_plot, color='green',label="擬合直線",linewidth=2) plt.legend(loc='lower right') plt.show()原文鏈接
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