Exponential Smoothing Methods for Time Series Forecasting 1. Introduction to Exponential Smoothing Exponential Smoothing is a time series forecasting technique that applies exponentially decreasing weights to past observations. It is….
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…..
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….
Polynomial Regression in Machine Learning 1. Introduction to Polynomial Regression Polynomial Regression is an extension of Linear Regression that models the relationship between the independent variable (X) and the dependent….
Linear Regression in Machine Learning 1. Introduction to Linear Regression Linear Regression is one of the most fundamental and widely used supervised learning algorithms in machine learning. It is primarily….
Underfitting vs Overfitting in Machine Learning Introduction One of the biggest challenges in machine learning is building a model that can generalize well to unseen data. The two common problems….
Bias-Variance Tradeoff in Machine Learning Introduction The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two sources of error that affect model performance: Understanding….
