query: How to compare features and classifiers which achieve perfect accuracy?
positive_passages.docid: 156676
positive_passages.text: How to compare features and classifiers which achieve perfect accuracy?
negative_passages.docid: 79498
negative_passages.text: Best methods to solve class imbalance problem and why?
negative_passages.docid: 26149
negative_passages.text: Logistic regression vs. LDA as two-class classifiers
negative_passages.docid: 25399
negative_passages.text: Predicting with both continuous and categorical features
negative_passages.docid: 102548
negative_passages.text: How to assess the similarity of two histograms?
negative_passages.docid: 102744
negative_passages.text: Evaluating models by Loglogss, AUC, and Accuracy
negative_passages.docid: 22203
negative_passages.text: Data normalization and standardization in neural networks
negative_passages.docid: 155013
negative_passages.text: How to validate k-fold cross validation results for classifiers?
negative_passages.docid: 77590
negative_passages.text: Highly unbalanced test data set and balanced training data in classification
negative_passages.docid: 144118
negative_passages.text: Area under curve of ROC vs. overall accuracy
negative_passages.docid: 126595
negative_passages.text: Does feature selection help improve the performance of machine learning?
negative_passages.docid: 135987
negative_passages.text: Better accuracy with validation set than test set
negative_passages.docid: 45881
negative_passages.text: Methods to work around the problem of missing data in machine learning
negative_passages.docid: 120166
negative_passages.text: How to correctly use validation and test sets for Neural Network training?
negative_passages.docid: 6314
negative_passages.text: How can I classify data with the nearest-neighbor algorithm using Python?
negative_passages.docid: 55917
negative_passages.text: Verifying representativeness of a sample, after simple random sampling
negative_passages.docid: 64175
negative_passages.text: How to estimate confidence level for SVM or Random Forest?