Scores

There are 2 types of scores: RegressionScores and ClassificationScores. Instead of using them directly, you should use the class methods of these two classes:

  • For RegressionScores, create it via RegressionScores.get_scores
  • For ClassificationScores, create it via ClassificationScores.get_scores

RegressionScores

Contains the following metrics:

  • mse (mean square error)
  • rmse (root mean square error)
  • rmsle (root mean square logarithmic error)
  • mae (mean absolute error)
  • r2 (r squared)
  • deviance (deviance, but here it's the same as r2)
  • mape (mean absolute percentage error)
  • wmape (weighted mean absolute percentage error)

ClassificationScores

Could be either BinomialScores or MultinomialScores.

BinomialScores

Contains the following metrics:

  • logloss
  • mean_per_class_error (mean per class error)
  • misclassification
  • auc (area under curve)
  • lift_top_group

MultinomialScores

Contains the following metrics:

  • logloss
  • mean_per_class_error (mean per class error)