In this article, I will try to interpret the Linear Regression, Lasso, and Decision Tree models which are inherently interpretable. I will analyze global interpretability -- which analyzes the most important feature for prediction in general and local interpretability -- which explains individual prediction results. Machine learning models are used in applications such as fraud and risk detection in bank transactions, voice assistants, recommendation systems, chatbots, self-driving cars, social network analysis, etc. However, sometimes it is difficult to interpret them because the algorithm represents a black box(e.g. So we need additional techniques to analyze black box decisions.

The acronym LIME stands for Local Interpretable Model-agnostic Explanations. The project is about explaining what machine learning models are doing (source). LIME supports explanations for tabular models, text classifiers, and image classifiers (currently). In a nutshell, LIME is used to explain predictions of your machine learning model. The explanations should help you to understand why the model behaves the way it does.

The'SHapley Additive exPlanations' Python library, better knows as the SHAP library, is one of the most popular libraries for machine learning interpretability. The SHAP library uses Shapley values at its core and is aimed at explaining individual predictions. But wait – what are Shapley values? Simply put, Shapley values are derived from Game Theory, where each feature in our data is a player, and the final reward is the prediction. Depending on the reward, Shapley values tell us how to distribute this reward among the players fairly. We won't cover this technique in detail here, but you can refer to this excellent article explaining how Shapley values work: A Unique Method for Machine Learning Interpretability: Game Theory & Shapley Values! The best part about SHAP is that it offers a special module for tree-based models. Considering how popular tree-based models are in hackathons and in the industry, this module makes fast computations, even considering dependent features.

Part 4 -- Is it possible to resolve the trade-off between bias and variance?