query: Does it make sense to up-sample imbalanced data if I care about predicting the right probabilities?
positive_passages.docid: 23761
positive_passages.text: When is unbalanced data really a problem in Machine Learning?
negative_passages.docid: 80014
negative_passages.text: Should I use t-test on highly skewed data ? Scientific proof, please?
negative_passages.docid: 124161
negative_passages.text: Why is Bayes Classifier the ideal classifier?
negative_passages.docid: 129169
negative_passages.text: Transforming positively skewed data with positive and negative values
negative_passages.docid: 17834
negative_passages.text: Calculating the optimal number of bins for severely skewed data
negative_passages.docid: 66215
negative_passages.text: Training a decision tree against unbalanced data
negative_passages.docid: 127708
negative_passages.text: Rolling analysis with out-of sample
negative_passages.docid: 121031
negative_passages.text: What's the accuracy of data obtained through a random sample?
negative_passages.docid: 129313
negative_passages.text: Testing for significant differences in skewness
negative_passages.docid: 52894
negative_passages.text: How to test if these two samples are from the same distribution?
negative_passages.docid: 48849
negative_passages.text: How to compare (probability) predictive ability of models developed from logistic regression?
negative_passages.docid: 11226
negative_passages.text: Can a probability distribution value exceeding 1 be OK?
negative_passages.docid: 17469
negative_passages.text: weighted cross entropy for imbalanced dataset - multiclass classification
negative_passages.docid: 111953
negative_passages.text: Independent samples t-test with unequal sample sizes
negative_passages.docid: 89953
negative_passages.text: problems in doing logistic regression with unbalanced sample, give me some references
negative_passages.docid: 142752
negative_passages.text: Should quantitative predictors be transformed  to be normally distributed?
negative_passages.docid: 67179
negative_passages.text: AUC and class imbalance in training/test dataset