query: Can you add the probabilities of a classifier to better predict an outcome?
positive_passages.docid: 155721
positive_passages.text: would adding the probabilities in a dataset be more accurate than the individual results?
negative_passages.docid: 49421
negative_passages.text: Can a decision in a node of a decision tree be based on comparison between 2 columns of the dataset?
negative_passages.docid: 90944
negative_passages.text: Why do we want probabilities to be *countably* additive?
negative_passages.docid: 138537
negative_passages.text: Training N classifiers for N labels vs one classifier with N labels
negative_passages.docid: 156460
negative_passages.text: Expanding a dataset based on a weight field
negative_passages.docid: 141640
negative_passages.text: Sklearn - Choosing the right model for supervised learning/classification task
negative_passages.docid: 54879
negative_passages.text: Using x to predict y when they are highly correlated
negative_passages.docid: 139640
negative_passages.text: Bias the classification in logistic regression
negative_passages.docid: 88274
negative_passages.text: Is it possible for test error to be lower than training error
negative_passages.docid: 47635
negative_passages.text: Combining probabilities/information from different sources
negative_passages.docid: 151342
negative_passages.text: Is there an optimal loss function for dealing with imbalanced classes?
negative_passages.docid: 48226
negative_passages.text: How to adjust the hyperparameters of MLP classifier to get more perfect performance
negative_passages.docid: 15613
negative_passages.text: Interpretation of simple predictions to odds ratios in logistic regression
negative_passages.docid: 25399
negative_passages.text: Predicting with both continuous and categorical features
negative_passages.docid: 30696
negative_passages.text: Prediction in Cox regression
negative_passages.docid: 79014
negative_passages.text: Probability cut-off value for Logistic Regression
negative_passages.docid: 90647
negative_passages.text: Is it possible to append training data to existing SVM models?