stacking-manual 0.1.3

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Motivation: Sometimes we train multiple models for different context in the data, for example: - We want to build many independent linear models - Model input table has a block of observations with NULLS in some features, we want two (or more) independent models; for data with nulls vs without

But, as we build seporate models, we have several challenges: - It’s hard to keep track of overall (combined) model performance. Often we resort to reporting performance on models individually - Many MLOps performance monitoring systems - such as MLFlow - are strucutred to track a single model object, and having multiple independent model objects can make the interface unwieldy - We may resort to doing training and model inference in one shot without saving the model object, since running a training pipeline, than inference pipeline requires saving and loading many models, which is hard to keep track of

This library helps combine models (also known as Stacking) when you want to explicitly assign the models to fit and predict on specific observations. Currently sklearn stacking module does not allow for explicitly assigning models or independent model training