from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) # Out[6]: # LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) y_predict = lin_reg.predict(X) plt.scatter(x, y) plt.plot(x, y_predict, color='r') ...
Draw the line of polynomial regression:plt.plot(myline, mymodel(myline)) Display the diagram:plt.show() R-SquaredIt is important to know how well the relationship between the values of the x- and y-axis is, if there are no relationship the polynomial regression can not be used to ...
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=== 编辑于...
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...
机器学习使计算机从研究数据和统计数据中学习机器学习是向人工智能(AI)方向迈进的一步。机器学习是一个分析数据并学习预测结果的程序。本文主要介绍Python 机器学习 多项式回归(Polynomial Regression)。 原文地址:Python 机器学习 多项式回归(Polynomial Regression)...
Well – that’s where Polynomial Regression might be of assistance. In this article, we will learn about polynomial regression, and implement a polynomial regression model using Python.If you are not familiar with the concepts of Linear Regression, then I highly recommend you read this article ...
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: ...
model = LinearRegression() model.fit(X_poly,y) #chosen parameter param_val = z #predict quality based on the chosen hour y_pred_p = model.predict(poly_feat.fit_transform([[param_val]])) #Plot a range of predictions and actuals X_line = np.arange(min(X), max(X), 0.01) X_line...
from sklearn.linear_modelimportLinearRegressionX=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: ...
Python Copy poly_model = make_pipeline(PolynomialFeatures(2), LinearRegression()) poly_model.fit(df['log_ppgdp'][:, np.newaxis], df['lifeExpF']) predictions = poly_model.predict(df['log_ppgdp'][:, np.newaxis]) r2_score(df['lifeExpF'], predictions) ...