4 Python Libraries For Getting Better Model Interpretability
Model interpretability is the ability to approve and interpret the decisions of a predictive model in order to enable transparency in the decision-making process. By model interpretation, one can be able to understand the algorithmic decisions of a machine learning model. In this article, we list down 4 python libraries for model interpretability. (The list is in no particular order) 1| LIME Local Interpretable Model-agnostic Explanations (LIME) is a popular python library which can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model. The goal of LIME is to identify an interpretable model over the interpretable representation that is locally faithful to the classifier. LIME is able to explain any black box classifier, with two or more classes. Currently, LIME supports two types of inputs, they are tabular data and text data. Click here to know more. 2| SHAP SHapley Additive exPlanations (SHAP)  is a unified python library to explain the output of any machine learning model. It connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations. SHAP values rely on conditional expectations which is why it is needed to decide how to handle correlated (or otherwise dependent) input features. Tree SHAP algorithm is a fast and exact method to estimate SHAP values for tree models and ensembles of trees, under several different possible assumptions about feature dependence. It is used to explain the output of ensemble tree models. Click here to know more. 3| ELI5 ELI5 is a Python library which allows to visualize and debug various Machine Learning models using unified API. It has built-in support for several ML frameworks and provides a way to explain black-box models. This python package helps to debug machine learning classifiers and explain their predictions. It provides support for the machine learning frameworks and packages such as sci-kit learn, XGBoost, LightGBM, CatBoost, etc. This library also implements several algorithms for inspecting black-box models such as TextExplainer which allows explaining predictions of any text classifier using the LIME algorithm and Permutation importance method which can be used to compute feature importance for black boxes estimators. ELI5 aims to handle not only simple cases but even for simple cases having a unified API for inspection. Click here to know more. 4| Skater Skater is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). It is a unified framework which enables model interpretation for all forms of the model in order to build an interpretable machine learning system which is often needed for real-world use-cases. The library is still in beta phase but the model interpretability can be enabled in multiple ways. Click here to know more.
Model interpretability is the ability to approve and interpret the decisions of a predictive model in order to enable transparency in the decision-making process. By model interpretation, one can be able to understand the algorithmic decisions of a machine learning model. In this article, we list down 4 python libraries for model interpretability. (The list […]
Model interpretability is the ability to approve and interpret the decisions of a predictive model in order to enable transparency in the decision-making
["eli5", "Python", "SHAP"]
["SHAP", "machine learning", "eli5", "TPU", "AI", "ML", "Python", "Aim", "XGBoost", "CatBoost", "LightGBM", "R"]
["AI", "machine learning", "ML", "Aim", "XGBoost", "LightGBM", "CatBoost", "TPU", "Python", "R"]
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