Dipayon Paul

Random Forests have emerged as a cornerstone of modern machine learning, known for their high accuracy, robustness, and ease of use. This article delves into the intricacies of Random Forests, exploring their underlying principles, applications, advantages, limitations, and providing Python code examples to illustrate their practical implementation. 1. Introduction to

Decision trees are a popular machine learning algorithm known for their simplicity and interpretability. They are used for both classification and regression tasks, making them a versatile tool for various applications. This article delves into the inner workings of decision trees, exploring how they split data, make predictions, and the

Logistic regression is a fundamental and widely used statistical and machine learning technique for classification problems. Unlike linear regression, which predicts continuous outcomes, logistic regression predicts the probability that an instance belongs to a particular class. It's a powerful and interpretable method, particularly well-suited for binary classification (two

Linear regression is a fundamental and widely used statistical and machine learning technique for modeling the relationship between a dependent variable (also called the target or response variable) and one or more independent variables (also called predictors, features, or explanatory variables). It assumes a linear relationship between the variables, meaning

In machine learning, the ultimate goal is to build models that generalize well to unseen data. We want our models to perform accurately not just on the data they were trained on, but also on new, real-world data. Simply evaluating a model on the same data it was trained on