k-Nearest Neighbors (k-NN)
k-Nearest Neighbors (k-NN) Algorithm in Machine Learning 1. Introduction to k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) is a supervised learning algorithm used for classification and regression tasks. It is a….
Tag: Bias-Variance Tradeoff
Support Vector Machines (SVM)
Support Vector Machines (SVM) in Machine Learning 1. Introduction to Support Vector Machines (SVM) Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression problems. SVM….
Random Forests
Random Forests in Machine Learning 1. Introduction to Random Forests Random Forest is a Supervised Machine Learning algorithm that is used for both Classification and Regression tasks. It is an….
Decision Trees
Decision Trees in Machine Learning 1. Introduction to Decision Trees A Decision Tree is a Supervised Learning algorithm used for both classification and regression problems. It mimics human decision-making by….
Logistic Regression
Logistic Regression in Machine Learning 1. Introduction to Logistic Regression Logistic Regression is a Supervised Learning algorithm used for classification problems. Unlike Linear Regression, which predicts continuous values, Logistic Regression….
Polynomial Regression
Polynomial Regression in Machine Learning 1. Introduction to Polynomial Regression Polynomial Regression is an extension of Linear Regression that models the relationship between the independent variable (X) and the dependent….
Linear Regression
Linear Regression in Machine Learning 1. Introduction to Linear Regression Linear Regression is one of the most fundamental and widely used supervised learning algorithms in machine learning. It is primarily….
Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Model Evaluation Metrics in Machine Learning Evaluating a machine learning model is crucial for ensuring its effectiveness. Model evaluation metrics provide a way to measure performance, compare models, and fine-tune….
Underfitting vs Overfitting
Underfitting vs Overfitting in Machine Learning Introduction One of the biggest challenges in machine learning is building a model that can generalize well to unseen data. The two common problems….
Bias-Variance Tradeoff
Bias-Variance Tradeoff in Machine Learning Introduction The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two sources of error that affect model performance: Understanding….