What is Gradient Descent? Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of the negative gradient. It is widely used in machine learning for optimizing cost functions and finding the best parameters…
Unlocking Decision-Making: An In-Depth Analysis of Entropy in Decision Trees
Decision trees are a popular machine learning algorithm used for classification and regression tasks. They work by splitting data into subsets based on feature values, ultimately leading to decisions. A crucial concept in decision trees is entropy, which measures the impurity or…
Building a K-Means Algorithm in Python: A Step-by-Step Guide
K-Means Clustering is a popular unsupervised machine learning algorithm used for grouping data into clusters. It aims to partition a dataset into k distinct, non-overlapping groups (or clusters) based on the similarity of the data points. The algorithm works by: K-Means is widely used…
K-Nearest Neighbors Explained: A Guide to Classification Algorithms
K-Nearest Neighbors (KNN) is a simple yet powerful algorithm used for classification and regression tasks in machine learning. This article will explore the KNN algorithm, its implementation using the Iris dataset, and the underlying mathematics that make it effective. What is…
Building a Decision Tree from Scratch: Gini Impurity Explained with Python
Decision Trees and Gini Impurity: A Fun Dive into Data Science Hello, my fellow data enthusiasts! Buckle up because today we’re venturing into the magical world of decision trees and Gini impurity. Don’t worry this isn’t some dry, soul-sucking math lecture. Nope! We’re going…