Contextualized
An SKLearn-style toolbox for estimating and analyzing models, distributions, and functions with context-specific parameters
An SKLearn-style toolbox for estimating and analyzing models, distributions, and functions with context-specific parameters
State-of-the-art methods to estimate and explore context-specific models
Comprehensive support for multiple types of models and meta-models, with training and analysis tools
Building blocks to support extending meta-models to contextualize your application-specific model
Access state-of-the-art techniques through an open unified API and rich visualizations.
Understand models using a wide range of explainers and techniques using interactive visuals. Choose your algorithm and easily start experimenting in seconds.
Join the community, request and contribute improvements to make your model the best it can be.
Do the features which predict an outcome change from sample to sample?
Does the ordering of feature interactions change from sample to sample?
Do the correlations between features change from sample to sample?
Understand heterogeneity hiding in your dataset.
Understand heterogenetiy in the observed populations.
Build models which adapt to new environments and contexts.
Contextualize your novel models.
We encourage you to join the effort and contribute feedback, algorithms, ideas and more, so we can improve the toolkit together!
Contribute