With the advancement in machine learning and deep learning algorithms , models now have prediction accuracy, process efficiency, and research productivity . However, these are black box and generally don’t explain their prediction .This becomes a barrier to the adoption of machine learning models especially in world of finance where explainable AI is usually a regulatory requirement.
There are many method which help in improving the explainability ofthe model predictions such as SHAP,LIME and Permutation importance .Today we will discuss the theory and practical application of one such method — LIME.

Photo by Geronimo Giqueaux on Unsplash

LIME

Introduction

Lime is a post hoc technique, which means…


I have always wondered how robust XGBoost is to correlation among independent variables. Should one check for multicollinearity before building an XGBoost model? In this post I will cover the impact of correlation on XGboost by using two datasets from Kaggle — Credit Fraud Data and BNP Paribas Cardif Claims Management

Photo by The Creative Exchange on Unsplash

Introduction

Correlation is a statistical measure that expresses the extent to which two variables are linearly related (i.e. they change together at a constant rate). It’s a common tool for describing simple relationships without making a statement about cause and effect. …

Vishesh Gupta

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store