Machine Learning models and algorithms

This is a list of machine learning models and algorithms, with links to library implementations.

Contents

AdaBoost

  • Boosting
  • Ensemble

Affinity Propagation

  • Clustering
  • Unsupervised

Apriori

  • Association rules learning

Averaged One-Dependence Estimators (AODE)

  • Bayesian
  • Classification

Averaged One-Dependence Estimators (AODE) with Subsumption Resolution

  • Bayesian
  • Classification

Bagging

  • Averaging
  • Ensemble

Bayesian Logistic Regression

  • Bayesian
  • Regression

Bayesian Network (BN)

  • Bayesian
  • Classification

Bernoulli Naive Bayes

  • Bayesian
  • Classification

C4.5 and C5.0

  • Classification
  • Decision tree
  • Regression
  • Supervised

Chi-squared Automatic Interaction Detection (CHAID)

  • Classification
  • Decision tree
  • Regression
  • Supervised

Classification And Regression Tree (CART)

  • Classification
  • Decision tree
  • Regression
  • Supervised

Conditional Decision Trees

  • Classification
  • Decision tree
  • Regression
  • Supervised

DBSCAN

  • Clustering

Decision Stump

  • Classification
  • Decision tree
  • Regression
  • Supervised

Discriminative Multinomial Naive Bayes

  • Bayesian
  • Classification

Eclat

  • Association rules learning

Elastic Net

  • Regression
  • Regularisation

Expectation Maximisation (EM)

  • Clustering
  • Unsupervised

Feedforward Network

  • Neural network

Gaussian Naive Bayes

  • Bayesian
  • Classification

Gaussian Processes

  • Regression

Gradient Boosted Trees (GBT)

  • Boosting
  • Ensemble

Hidden Naive Bayes

  • Bayesian
  • Classification

Hierarchical Clustering

  • Clustering
  • Unsupervised

Isotonic Regression

  • Regression

Iterative Dichotomiser 3 (ID3)

  • Classification
  • Decision tree
  • Regression
  • Supervised

K-Means

  • Clustering
  • Unsupervised

K-Medians

  • Clustering
  • Unsupervised

K-Nearest Neighbour

  • Classification
  • Instance based

Kernel Perceptron

Learning Vector Quantization (LVQ)

  • Instance based
  • Neural network
  • Unsupervised

Least Absolute Shrinkage And Selection Operator (LASSO)

  • Regression
  • Regularisation

Least Median of Squares Regression

  • Regression

Least-Angle Regression (LARS)

  • Regression
  • Regularisation

Locally Weighted Learning (LWL)

  • Instance based
  • Regression

Logistic Regression

  • Classification

M5

  • Classification
  • Decision tree
  • Regression
  • Supervised

Mean Shift

  • Clustering
  • Unsupervised

Multilayer Perceptron

  • Neural network

Multinomial Naive Bayes

  • Bayesian
  • Classification

Naive Bayes

  • Bayesian
  • Classification

Ordinary Least Squares

  • Regression

Perceptron

  • Neural network

Polynomial Regression

  • Regression

Radial Basis Function (RBF) Networks

  • Instance based
  • Neural network
  • Regression

Random Forest

  • Averaging
  • Ensemble

Recurrent Neural Network

  • Neural network

Ridge Regression

  • Regression
  • Regularisation

Self-Organizing Map (SOM), Kohonnen Network

  • Instance based
  • Neural network
  • Unsupervised

Spectral Clustering

  • Clustering
  • Unsupervised

Support Vector Machine (SVM)

  • Classification
  • Instance based
  • Kernel machine
  • Regression
  • Supervised

Voted Perceptron

  • Neural network

Weightily Averaged One-Dependence Estimators

  • Bayesian
  • Classification