Explainable AI (XAI)
Explainable AI (XAI): A Comprehensive Guide Introduction to Explainable AI (XAI) Explainable AI (XAI) refers to a set of processes and methods that enable humans to understand and trust the….
Tag: Feature Importance
Model Interpretability Techniques
Model Interpretability Techniques in Machine Learning 1. Introduction to Model Interpretability Machine learning models are often considered “black boxes”, meaning it’s difficult to understand how they make predictions. However, in….
Gradient Boosting (XGBoost, LightGBM, CatBoost)
Gradient Boosting (XGBoost, LightGBM, CatBoost) in Machine Learning 1. Introduction to Gradient Boosting Gradient Boosting is a powerful ensemble learning technique used in machine learning for classification and regression tasks…..
Random Forests
Random Forests in Machine Learning 1. Introduction to Random Forests Random Forest is a Supervised Machine Learning algorithm that is used for both Classification and Regression tasks. It is an….
Decision Trees
Decision Trees in Machine Learning 1. Introduction to Decision Trees A Decision Tree is a Supervised Learning algorithm used for both classification and regression problems. It mimics human decision-making by….
Feature Selection Techniques
Feature Selection Techniques: A Comprehensive Guide Introduction Feature selection is a crucial step in machine learning that involves selecting the most relevant features (variables) for building an efficient and accurate….