get_loc(i)) for i in x.columns] vif 代码语言:javascript 代码运行次数:0 运行 AI代码解释 x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2021) clf=LogisticRegression(max_iter=300) clf.fit(x_train,y_train) y_pred=clf.predict(x_test) accuracy_score(...
首先建立全量回归模型 lm1 <- lm(GNP.deflator ~ ., data = longley) summary(lm1) ## ## Call: ## lm(formula = GNP.deflator ~ ., data = longley) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.009 -0.515 0.113 0.423 1.550 ## ## Coefficients: ## Estimate Std. Error t value...
4.8.1 ✌ 删除 账户资金 x=x.drop(columns=['账户资金(元)'])x=pd.DataFrame(x)y=yvif=[variance_inflation_factor(x.values,x.columns.get_loc(i)) for i in x.columns]vif x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2021)clf=LogisticRegression(max_...
)X3=np.column_stack([X1,np.random.rand(100,2)])import matplotlib.pyplot as pltclf=LinearRegression()vif2=np.zeros((10,1))for i in range(10):tmp=[k for k in range(10) if k!=i]clf.fit(X2[:,tmp],X2[:,i])vifi=1/(1-clf.score(X2[:,tmp],X2[:,i]))vif2[i]=vifi ...
vif=[variance_inflation_factor(x.values,x.columns.get_loc(i)) for i in x.columns] vif 1. 2. 3. 4. 5. AI检测代码解析 x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2021) clf=LogisticRegression(max_iter=300) ...
Python实现逻辑回归(Logistic Regression in Python) : 参考文献: scikit-learn文档:http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression 纯Python实现logistc回归:http://blog.smellthedata.com/2009/06/python-logistic-regression-with-l2.html ...
# 画图importseabornassns# 制作数据集fromsklearn.datasetsimportmake_blobs# VIF膨胀因子fromstatsmodels.stats.outliers_influenceimportvariance_inflation_factor# 分割数据集fromsklearn.model_selectionimporttrain_test_split# 逻辑回归fromsklearn.linear_modelimportLogisticRegression# AUC和准确度fromsklearn.metricsimpor...
VIF regression is a fast algorithm whichdoes feature selection in large regression problems. VIF regression handles big number of features streamwise. Such streamwise regression method has its advantages over traditional stepwise regression as it offers fastercomputational speed without loss of its accuracy...
We propose a fast and accurate algorithm, VIF regression, for doing feature selection in large regression problems. VIF regression is extremely fast; it uses a one-pass search over the predictors and a computationally efficient method of testing each potential predictor for addition to the model. ...
VIF Regression: A SAS® Application to Feature Selection in Large Data Sets SAS platform or server which usually stores theirbig data sets and also from the VIF regression, a much-needed fast feature selection for large data ... R Zhang,SII;F Liu 被引量: 0发表: 0年 On the Use of ...