Introduction Gradient Boosting algorithms, such as XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine), are among the most powerful machine learning techniques used for both classification and regression tasks. These algorithms build strong predictive models by combining multiple weak models (usually Decision Trees) in an additive manner. They focus on minimizing errors made
Introduction Decision Trees and Random Forests are powerful machine learning algorithms widely used for both classification and regression tasks. These models are intuitive, easy to interpret, and capable of handling complex datasets with minimal preprocessing. While Decision Trees provide a simple and transparent approach, Random Forests enhance their performance by creating an ensemble of trees,
Introduction Support Vector Machines (SVMs) are powerful supervised machine learning models used primarily for classification and regression tasks. Introduced in the 1990s, SVMs have since become one of the most popular techniques in machine learning, known for their efficiency in handling complex, high-dimensional data. SVMs work by finding the hyperplane that best divides a dataset