Local Interpretable Model-Independent Explanations is a widely used library that approximates sophisticated machine learning models with interpretable local models to aid in their interpretation. It creates perturbed instances close to a given data point and tracks how these instances affect the model’s predictions.
can shed light on the model’s behavior for particular data points by fitting a straightforward, interpretable model to these perturbed instances.A Python package called Explain Like I’m 5 seeks to give clear justifications for machine learning models. It provides feature importance using a variety of methodologies, including permutation significance, tree-based importance and linear model coefficients, and it supports a wide range of models.