House Price Prediction
House Price Prediction: A Comprehensive Guide House price prediction is a classic machine learning problem that involves estimating the price of a house based on various features such as location,….
Tag: Gradient Boosting
Credit Scoring Models
Credit Scoring Models: A Comprehensive Guide Introduction Credit scoring models are statistical and machine learning models used by financial institutions to assess the creditworthiness of individuals and businesses. These models….
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….
Confusion Matrix
Confusion Matrix in Machine Learning 1. Introduction to Confusion Matrix A Confusion Matrix is a performance evaluation metric used in classification problems. It helps to understand how well a machine….
ROC Curve and AUC
ROC Curve and AUC in Machine Learning The ROC (Receiver Operating Characteristic) Curve and AUC (Area Under the Curve) are essential metrics for evaluating the performance of classification models, especially….
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….