June 2025

A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks. It has hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes. note: Parametric supervised learning refers to a type of machine learning where the model assumes a specific functional form and estimates […]

Boosting Accuracy with Voting Classifiers In machine learning, combining multiple models often leads to better performance than relying on a single one. A Voting Classifier is a simple ensemble method that does just that — it aggregates predictions from several models to improve accuracy. There are two types: Hard Voting: Takes the majority vote from […]

Elastic Net regression is a linear regression method that merges the strengths of both Lasso (L1) and Ridge (L2) regression techniques. It helps reduce overfitting and is especially effective when working with datasets that have many features, particularly when some of those features are highly correlated. The model’s regularization is controlled by two key hyperparameters: […]

Boosting in machine learning is a technique that combines multiple simple models, often decision trees into a single, stronger model. It works with regression trees and improves performance by sequentially learning from the mistakes of previous models. According to the scikit-learn documentation, at each stage, a regression tree is fit on the negative gradient of […]

Random forest regressor is a variant of the random forest classifier. It is primarily used for classification tasks. This model is an ensemble of decision trees. It combines the predictions of multiple individual trees to imrpove performance. By aggregating the results from those trees, typically through votng or avaeraging. It produces a final prediction that […]