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....
from sklearn.model_selection import train_test_split np.random.seed(666) X_train, X_test, y_train, y_test = train_test_split(X, y) from sklearn.linear_model import LinearRegression from sklearn.pipeline import Pipeline from sklearn.preprocessing import PolynomialFeatures from sklearn.pre...
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_boston() # 提取特征和目标变量 X = boston....
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*...
在Python中,可以使用scikit-learn库来方便地实现多项式回归。以下是一个简单的实现示例: import numpy as np from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score # 生成示...
for linear regression 西瓜书54的例子解释得很好,也是我们最常见的形式 WX+B 所有变量的次数都是1,w1x1+w2x2+w3x3+...b, sklearn 操作如下: https://zhuanlan.zhihu.com/p/71633922 for polynomial,例子及其 coding 如下: https://www.cnblogs.com/Belter/p/8530222.html ==...
from sklearn.linear_modelimportLinearRegression X=x.reshape(-1,1)lin_reg=LinearRegression()lin_reg.fit(X,y)y_pred=lin_reg.predict(X)plt.scatter(x,y)plt.scatter(x,y_pred,color='r')plt.show() 可见用线性回归去拟合明显不好。为了解决这个问题,可以增加一个X的平方的特征: ...
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() 也可以写到pipeline中调用,会更方便: 代码语言:javascript 复制 from sklearn.pipelineimportPipeline from sklearn.preprocessingimportStandardScaler ...
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,...
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 **2+ X +2+ np.random.randn(m,1) ...