generalized-additive-models

Base Boosting

In the world of data science, base boosting is a technique used in multi-target regression to improve the accuracy of prediction models. What is Base Boosting? Base boosting allows for prior knowledge to be incorporated into the learning mechanism of already existing gradient boosting models. In simpler terms, it allows the model to learn from past mistakes and adjust its predictions based on known information. The method involves building an additive expansion in a set of elementary basis f

Neural Additive Model

Neural Additive Models (NAMs) are a type of machine learning model that are designed to be both accurate and easy to interpret. They are a part of a larger model family called Generalized Additive Models (GAMs), which make restrictions on the structure of neural networks so that the resulting models are more easily understood by humans. How NAMs Work The idea behind NAMs is relatively simple. They learn a linear combination of networks, meaning they combine the results of multiple neural netw

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