Importance

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Abstract

Techniques are described herein that are capable of generating a machine learning model based on constrained decision trees using a judgmental sample and feature ranking. A judgmental sample including observations, which include respective subsets of features, is generated. The observations are selected using multivariate stratified sampling. Important subsets of the features are determined based on each important subset being designated as more important than the other features by a respective individual. A score is determined for each feature, indicating a proportion of the important subsets that includes the respective feature. A highest scored feature is identified. Constrained decision trees having respective first splits are generated, based on respective subsets of the observations. A proportion of the first splits corresponding to the highest scored feature is based at least on the score of the highest scored feature. A machine learning model is generated based at least on the constrained decision trees.

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