normalized gini

Author: mathias-von-ottenbreit

API_REFERENCE_FOR_REGRESSION.md

@@ -50,7 +50,7 @@ Limits 1) the number of terms already in the model that can be considered as int
 Specifies the variance power when ***loss_function*** is "tweedie". Specifies a dispersion parameter when ***loss_function*** is "negative_binomial", "cauchy" or "weibull". 
 
 #### validation_tuning_metric (default = "default")
-Specifies which metric to use for validating the model and tuning ***m***. Available options are "default" (using the same methodology as when calculating the training error), "mse", "mae", "negative_gini", "group_mse", "group_mse_by_prediction" and "custom_function". The default is often a choice that fits well with respect to the ***loss_function*** chosen. However, if you want to use ***loss_function*** or ***dispersion_parameter*** as tuning parameters then the default is not suitable. "group_mse" requires that the "group" argument in the ***fit*** method is provided. "group_mse_by_prediction" groups predictions by up to ***group_mse_by_prediction_bins*** groups and calculates groupwise mse. For "custom_function" see ***calculate_custom_validation_error_function*** below.
+Specifies which metric to use for validating the model and tuning ***m***. Available options are "default" (using the same methodology as when calculating the training error), "mse", "mae", "negative_gini" (normalized), "group_mse", "group_mse_by_prediction" and "custom_function". The default is often a choice that fits well with respect to the ***loss_function*** chosen. However, if you want to use ***loss_function*** or ***dispersion_parameter*** as tuning parameters then the default is not suitable. "group_mse" requires that the "group" argument in the ***fit*** method is provided. "group_mse_by_prediction" groups predictions by up to ***group_mse_by_prediction_bins*** groups and calculates groupwise mse. For "custom_function" see ***calculate_custom_validation_error_function*** below.
 
 #### quantile (default = 0.5)
 Specifies the quantile to use when ***loss_function*** is "quantile".

cpp/APLRRegressor.h

@@ -1700,7 +1700,7 @@ double APLRRegressor::calculate_validation_error(const VectorXd &predictions)
     else if (validation_tuning_metric == "mae")
         return calculate_mean_error(calculate_errors(y_validation, predictions, sample_weight_validation, "mae"), sample_weight_validation);
     else if (validation_tuning_metric == "negative_gini")
-        return -calculate_gini(y_validation, predictions, sample_weight_validation);
+        return -calculate_gini(y_validation, predictions, sample_weight_validation) / calculate_gini(y_validation, y_validation, sample_weight_validation);
     else if (validation_tuning_metric == "group_mse")
     {
         bool group_is_not_provided{group_validation.rows() == 0};

setup.py

@@ -27,7 +27,7 @@
 
 setuptools.setup(
     name="aplr",
-    version="10.6.2",
+    version="10.6.3",
     description="Automatic Piecewise Linear Regression",
     ext_modules=[sfc_module],
     author="Mathias von Ottenbreit",