V1.2.0

Updates in V1.2.0

• feature_discretization:

  • [Add] Add parameter decimal to class ChiMerge.ChiMerge(), which allows users to control the number of decimals of the feature interval boundaries.
  • [Add] Add data table to the feature visualization FeatureIntervalAdjustment.plot_event_dist().
  • [Add] Add function FeatureIntervalAdjustment.feature_stat() that computes the input feature's sample distribution, including the sample sizes, event sizes and event proportions of each feature value.

• feature_selection.FeatureSelection:

  • [Add] Add function identify_colinear_features() that identifies the highly-correlated features pair that may cause colinearity problem.
  • [Add] Add function unstacked_corr_table() that returns the unstacked correlation table to help analyze the colinearity problem.

• model_training.LogisticRegressionScoreCard:

  • [Fix] Alter the LogisticRegressionScoreCard class so that it now accepts all parameters of sklearn.linear_model.LogisticRegression and its fit() fucntion accepts all parameters of the fit() of sklearn.linear_model.LogisticRegression (including sample_weight)
  • [Add] Add parameter baseOdds for LogisticRegressionScoreCard. This allows users to pass user-defined base odds (# of y=1 / # of y=0) to the Scorecard model.

• model_evaluation.ModelEvaluation:

  • [Add] Add function pref_table, which evaluates the classification performance on differet levels of model scores . This function is useful for setting classification threshold based on precision and recall.

• model_interpretation:

  • [Add] Add functionScorecardExplainer.important_features()to help interpret the result of a individual instance. This function indentifies features who contribute the most in pusing the total score of a particular instance above a threshold.