model_evaluation

Module contains checks of model evaluation.

Classes

BoostingOverfit	Check for overfit caused by using too many iterations in a gradient boosted model.
CalibrationScore	Calculate the calibration curve with brier score for each class.
ConfusionMatrixReport	Calculate the confusion matrix of the model on the given dataset.
ModelInferenceTime	Measure model average inference time (in seconds) per sample.
ModelInfo	Summarize given model parameters.
MultiModelPerformanceReport	Summarize performance scores for multiple models on test datasets.
TrainTestPerformance	Summarize given model performance on the train and test datasets based on selected scorers.
RegressionErrorDistribution	Check for systematic error and abnormal shape in the regression error distribution.
RegressionSystematicError	Check the regression systematic error.
RocReport	Calculate the ROC curve for each class.
SegmentPerformance	Display performance score segmented by 2 top (or given) features in a heatmap.
SimpleModelComparison	Compare given model score to simple model score (according to given model type).
TrainTestPredictionDrift	Calculate prediction drift between train dataset and test dataset, using statistical measures.
WeakSegmentsPerformance	Search for segments with low performance scores.
UnusedFeatures	Detect features that are nearly unused by the model.
SingleDatasetPerformance	Summarize given model performance on the train and test datasets based on selected scorers.

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WholeDatasetDrift.to_json

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BoostingOverfit