We'll use this fact to use linear regression to model data that does not follow a straight line. Let's apply this to our model of log_ppgdp and lifeExpF.Python Copy from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=2) X = df['log_ppgdp'][:, np....
二,代码演示 from sklearn.preprocessing import PolynomialFeatures import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error np.random.seed(42)#固定每次随机结果,用来测试算法 m = 100 X = 6*np.random.rand(m,1) - 3 y = 0.5*...
其实在sklearn中有封装好的方法(sklearn.preprocessing.PolynomialFeatures),我们不必自己去生成这个特征了: fromsklearn.preprocessingimportPolynomialFeatures poly=PolynomialFeatures(degree=2)# 添加几次方特征poly.fit(X)X2=poly.transform(X)# 训练lin_reg=LinearRegression()lin_reg.fit(X2,y)y_pred=lin_reg.pr...
# -*- coding: utf-8-*-import matplotlib.pyplotasplt import numpyasnpfromsklearn.preprocessing import PolynomialFeaturesfromsklearn.linear_model import LinearRegressionif__name__ =='__main__': # generate a random dataset np.random.seed(42) m=100X=6* np.random.rand(m,1) -3y=0.5* X **...
0x1:Polynomial Regression(多项式回归) 1. 为什么我们需要多项式回归 线性回归模型是机器学习和数理统计中最简单也最常见的模型,但是线性回归有一个最重要的假设前提就是,响应变量和解释变量之间的确存在着线性关系,否则就无法建立有效(强拟合优度)的线性模型。
Python and the Sklearn module will compute this value for you, all you have to do is feed it with the x and y arrays:Example How well does my data fit in a polynomial regression? import numpyfrom sklearn.metrics import r2_scorex = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,...
8.机器学习sklearn---多项式回归(房价与房屋尺寸关系的非线性拟合) 1.基本概念多项式回归(PolynomialRegression)是研究一个因变量与一个或多个自变量间多项式的回归分析方法。如果自变量只有一个 时,称为一元多项式回归;如果自变量有多个时,称为多元多项式回归。1.在一元回归分析中,如果依变量y与自变量x的关系为非线性...
from sklearn.preprocessingimportPolynomialFeatures poly_regs= PolynomialFeatures(degree=2) x_poly= poly_regs.fit_transform(x) lin_reg_2 =LinearRegression() lin_reg_2.fit(x_poly, y) In the above lines of code, we have usedpoly_regs.fit_transform(x), because first we are converting our fea...
from sklearn.linear_model import LinearRegressionpoly_reg = PolynomialFeatures(degree = 4)X_poly = poly_reg.fit_transform(X)lin_reg = LinearRegression()lin_reg.fit(X_poly, y) The class “LinearRegression” is also imported and is assigned to the variable “lin_reg” which is fitted with...
Let’s start with Linear Regression first:# Importing Linear Regression from sklearn.linear_model import LinearRegression # Training Model lm=LinearRegression() lm.fit(x.reshape(-1,1),y.reshape(-1,1))Let’s see how linear regression performs on this dataset:...