Introduction Stochastic Gradient Descent (SGD) is one of the most widely used optimization algorithms in machine learning, particularly for training large-scale models such as deep neural networks. SGD is an iterative method used to minimize a loss function by adjusting the model parameters in the direction of the negative gradient. This makes it an essential…
Introduction Principal Component Analysis (PCA) is a powerful statistical technique widely used for dimensionality reduction and feature extraction in machine learning. It is particularly useful when dealing with high-dimensional data, where the number of features can be overwhelming and may lead to challenges such as overfitting, computational inefficiency, and interpretability issues. PCA helps mitigate these…
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…