ML Algorithm Documentation

Explore detailed explanations of major Machine Learning algorithms used in AI systems and prediction engines.

Documentation Index

Linear Regression Logistic Regression KNN SVM Decision Tree Random Forest Clustering Naive Bayes

Linear Regression

Supervised Learning Algorithm

Overview

Linear Regression is one of the most important supervised machine learning algorithms. It is used for predicting continuous numerical values based on historical data.

Formula

Y = mX + b

Applications

  • House Price Prediction
  • Stock Market Analysis
  • Sales Forecasting
  • Weather Prediction

Advantages

  • Simple and fast
  • Easy to interpret
  • Good for linear datasets
Test Linear Regression Model

Logistic Regression

Classification Algorithm

Overview

Logistic Regression is a supervised classification algorithm used for predicting categorical outputs. It mainly works for binary classification problems.

Sigmoid Function

σ(x) = 1 / (1 + e^-x)

Applications

  • Spam Detection
  • Fraud Detection
  • Medical Diagnosis
  • Email Classification
Test Logistic Regression Model

K-Nearest Neighbors (KNN)

Distance-Based Algorithm

Overview

KNN is a simple supervised learning algorithm that classifies data points based on the nearest neighbors around them.

How It Works

The algorithm calculates distances between data points and assigns the majority class among the nearest neighbors.

Applications

  • Recommendation Systems
  • Image Recognition
  • Pattern Detection
Test KNN Model

Support Vector Machine (SVM)

Classification Algorithm

Overview

SVM is a powerful machine learning algorithm used for classification and regression. It works by finding the optimal hyperplane that separates classes.

Applications

  • Face Detection
  • Text Classification
  • Image Classification
Test SVM Model

Decision Tree

Tree-Based Algorithm

Overview

Decision Trees split data into branches based on feature values. They are easy to visualize and interpret.

Applications

  • Risk Analysis
  • Customer Segmentation
  • Medical Diagnosis
Test Decision Tree Model

Random Forest

Ensemble Learning Algorithm

Overview

Random Forest combines multiple decision trees to improve prediction accuracy and reduce overfitting.

Applications

  • Stock Prediction
  • Fraud Detection
  • Medical Analysis
Test Random Forest Model

Clustering

Unsupervised Learning

Overview

Clustering groups similar data points together without labeled outputs. K-Means is one of the most popular clustering algorithms.

Applications

  • Customer Segmentation
  • Market Analysis
  • Pattern Recognition
Test Clustering Concepts

Naive Bayes

Probability-Based Algorithm

Overview

Naive Bayes is a classification algorithm based on Bayes theorem. It assumes all features are independent.

Applications

  • Spam Filtering
  • News Classification
  • Sentiment Analysis
Test Naive Bayes Concepts