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A model specialized for imbalanced class learning.

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Welcome to AdHocBoost--a model that is specialized for classification in a severely imbalanced-class scenario.


Many data science problems have severely imbalanced classes (e.g. predicting fraudulent transactions, predicting order-cancellations in food-delivery, predicting if a day in Berlin will be sunny). In these situations, predicting the positive class is hard! This module aims to alleviate some of that.

The AdHocBoost model works by creating n sequential models. The first n-1 models can most aptly be thought of as dataset filtering models, i.e. each one does a good job at classifying rows as "definitely not the positive class" versus "maybe the positive class". The nth model only works on this filtered "maybe positive" data.

Like this, the class imbalance is alleviated at each filter-step, such that by the time the dataset is filtered for final classification by the nth model, the classes are considerably more balanced.

Run Instructions

Installation is with pip install adhocboost. Beyond that, AdHocBoost conforms to a sklearn-like API: to use it, you simply instantiate it, and then use .fit(), .predict(), and .predict_proba() as you see... fit ;)

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