vif=[variance_inflation_factor(x.values,x.columns.get_loc(i)) for i in x.columns] vif 1. 2. 3. 4. 5. 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_...
4.1 ✌ 导入相关库 # 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f...
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(y_test,y_pred) 代码语言:javascript 复制 roc_auc_score(y_test,clf.predict_proba(x_test)[:,1])...
注:本文Logistic回归用了sklearn包,算法已经封装好了,如果想了解具体的实现方法,可以参考《机器学习实战》一书,或参考此博文是关于使用statesmodels的Logit函数: 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-re...
x=x.drop(columns=['累计交易佣金(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_iter=300)clf.fit(x_...
drop(remove,axis=1,inplace=True) vif = calculate_vif(df) feature_selected = vif.iloc[:-1,:]['index'].values X_new = df[feature_selected] y_new = y model = LogisticRegression() model.fit(X_new,y_new) model.score(X_new,y_new) # output 0.9507908611599297 print(X_new.shape) # ...
自动VIF(Variable Importance Factor)是一种用于变量分析的自动化方法。它用于评估多元回归模型中各个自变量之间的相关性和重要性,以帮助确定哪些变量对因变量的解释具有最大的贡献。 自动VIF通过计算每个自变量的VIF值来衡量其重要性。VIF值是一个衡量自变量之间相关性程度的指标,它表示一个自变量可以被其他自变量...
Coefficients, statistics of the factors (S.E.-standard error, VIF- variance inflation factor) and the multi-collinearity diagnosis indexes for variables used in the logistic regression equation.Jie, Dou
SVM were developed in an effort to develop artificial intelligence strategies for complex problems. SVM have mostly been applied to classification or regression problems. For classification purposes, SVMs aim to produce a mathematical n-dimensional space function capable of non-linearly distinguishing ...
Using Transatlantic Trends Survey data (2004-2010) through binary logistic regression analysis this article maps out this Turkish dilemma concerning NATO.Ebru Ş. CANAN-SOKULLU