import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error # 导入波士顿房屋数据集 boston = load_bos...
1、多项式回归(Polynomial Regression) 如果数据点显然不适合线性回归(所有数据点之间的直线),则可能是多项式回归的理想选择。 像线性回归一样,多项式回归使用变量x和y之间的关系来找到绘制数据点线的最佳方法。 2、多项式回归是如何工作的? Python提供了一些方法来查找数据点之间的关系并绘制多项式回归线。我们将向您展...
poly2_reg = PolynomialRegression(degree=2) poly2_reg.fit(X, y) """ Out[8]: Pipeline(memory=None, steps=[('poly', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('lin_reg', Linear...
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? importnumpy fromsklearn.metricsimportr2_score x =[1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,...
from sklearn.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.predict(X2)plt.scatter(x,y)plt.scatter(x,y_pred,color='r')plt.show() ...
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...
转换后的高次项回归 Xp=sm.add_constant(Xp)model=sm.OLS(y,Xp)results=model.fit()results.summary() 结果如下: Ref: 1,Polynomial Regression Using statsmodels.formula.api 2,statsmodels.regression.linear_model.OLS - statsmodels ===全文结束===...
Python Copy from sklearn.pipeline import make_pipeline poly_model = make_pipeline(PolynomialFeatures(2), LinearRegression()) X = df['log_ppgdp'][:, np.newaxis] y = df['lifeExpF'] poly_model.fit(X, y) x_min = df['log_ppgdp'].min() x_max = df['log_ppgdp'].max() x_plo...
机器学习sklearn(3)多项式回归 import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error...猜你喜欢【机器学习】多项式回归python实现 【机器学习】多项式回归原理...
The polynomial regression’s preprocessor is imported from the sklearn package as “sklearn.preprocessing.PolynomialFeatures” and the dataset is divided into training and test data in the ratio of 80:20. With polynomial regression, the RMSE obtained is 214.548, and R2 value as 0.602. Fig. 11.7...