query: Probability prediction using logistic regression for an unbalanced dataset
positive_passages.docid: 31300
positive_passages.text: Does an unbalanced sample matter when doing logistic regression?
negative_passages.docid: 114625
negative_passages.text: Model selection and cross-validation: The right way
negative_passages.docid: 98895
negative_passages.text: Discriminant analysis vs logistic regression
negative_passages.docid: 132510
negative_passages.text: From exp (coefficients) to Odds Ratio and their interpretation in Logistic Regression with factors
negative_passages.docid: 98625
negative_passages.text: theoretical basis for logistic regression
negative_passages.docid: 78651
negative_passages.text: Predictive model developement for logistic regression?
negative_passages.docid: 77815
negative_passages.text: Machine Learning with intended missing values
negative_passages.docid: 26149
negative_passages.text: Logistic regression vs. LDA as two-class classifiers
negative_passages.docid: 154994
negative_passages.text: Violation of linearity assumption in Logistic Regression
negative_passages.docid: 62731
negative_passages.text: Predicting count data with random forest?
negative_passages.docid: 102692
negative_passages.text: Is my logic straight? Logistic regressions in R
negative_passages.docid: 40442
negative_passages.text: Is up- or down-sampling imbalanced data actually that effective? Why?
negative_passages.docid: 144448
negative_passages.text: Understanding lack of fit in logistic regression
negative_passages.docid: 104182
negative_passages.text: regression for binary classification
negative_passages.docid: 9119
negative_passages.text: Multiple logistic regression power analysis
negative_passages.docid: 64602
negative_passages.text: Logistic regression: class probabilities
negative_passages.docid: 24750
negative_passages.text: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?