query: ROC and AUC for imbalanced data?
positive_passages.docid: 94048
positive_passages.text: ROC vs Precision-recall curves on imbalanced dataset
negative_passages.docid: 40442
negative_passages.text: Is up- or down-sampling imbalanced data actually that effective? Why?
negative_passages.docid: 116610
negative_passages.text: Filtering and Replacing outliers
negative_passages.docid: 150758
negative_passages.text: How does cross-validation overcome the overfitting problem?
negative_passages.docid: 64175
negative_passages.text: How to estimate confidence level for SVM or Random Forest?
negative_passages.docid: 113178
negative_passages.text: How is the optimal probability cut-off in a ROC defined by the R package Epi?
negative_passages.docid: 107278
negative_passages.text: Comparing SVM and logistic regression
negative_passages.docid: 50549
negative_passages.text: Why is ROC AUC equivalent to the probability that two randomly-selected samples are correctly ranked?
negative_passages.docid: 94227
negative_passages.text: ROC/AUC Confidence Interval
negative_passages.docid: 137314
negative_passages.text: ROC curve under diagonal?
negative_passages.docid: 36580
negative_passages.text: Outlier Detection on skewed Distributions
negative_passages.docid: 77815
negative_passages.text: Machine Learning with intended missing values
negative_passages.docid: 28164
negative_passages.text: Statistical significance (p-value) for comparing two classifiers with respect to (mean) ROC AUC, sensitivity and specificity
negative_passages.docid: 123308
negative_passages.text: How to interpret a ROC curve?
negative_passages.docid: 80251
negative_passages.text: Compare classification performance of two heuristics
negative_passages.docid: 48400
negative_passages.text: How to compute precision/recall for multiclass-multilabel classification?
negative_passages.docid: 77590
negative_passages.text: Highly unbalanced test data set and balanced training data in classification