Machine Learning - Algorithms Cheatsheet
Regression
- Ordinary Least Squares Regression(OLSR)
- Linear Regression
- Logistic Regression
- Stepwise Regression
- Multivariate Adaptive Regression Splines(MARS)
- Locally Estimated Scatterplot Smoothing(LOESS)
- Jackknife Regression
Regularization
- Ridge Regression
- Least Absolute Shrinkage and Selection Operator(LASSO)
- Elastic Net
- Least-Angle Regression(LARS)
Instance based, a.k.a. cake-based, memory-based
- k-Nearest Neighbour(kNN)
- Learning Vector Quantization(LVQ)
- Self-Organizing Map(SOM)
- Locally Weighted Learning(LWL)
Dimensionality Reduction
- Principle Component Analysis(PCA)
- Principle Component Regression(PCR)
- Partial Least Squares Regression(PLSR)
- Sammon Mapping
- Multidimensional Scaling(MDS)
- Projection Pursuit
- Discriminant Analysis(LDA, MDA, QDA, FDA)
Deep Learning
- Deep Boltzmann Machine(DBM)
- Deep Belief Networks(DBN)
- Convolutional Neural Network(CNN)
- Stacked Auto-Encoders
- RNN
Associated Rule
- Apriori
- Eclat
- FP-Growth
Ensemble
- Logit Boost(Boosting)
- Bootstrapped Aggregation(Bagging)
- AdaBoost
- Stacked Generalization(blending)
- Gradient Boosting machines(GBM)
- Gradient Boosted Regression Trees(GBRT)
- Random Forest
Bayesian
- Naive Bayes
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Averaged One-Dependence Estimators(AODE)
- Bayesian Belief Network(BBN)
- Bayesion Network(BN)
- Hidden Markov Models
- Conditional random fields(CRFs)
Decision Tree
- Classification and Regression Tree(CART)
- Iterative Dickotomiser 3(ID3)
- C4.5 and C5.0
- Chi-squared Automatic Interaction Detection(CHAID)
- Decision Stump
- M5
- Conditional Decision Trees
Clustering
- Single-linkage clustering
- k-Means
- k-Medians
- Expectation Maximization(EM)
- Hierarchical Clustering
- Fuzzy clustering
- DBSCAN
- OPTICS algorithm
- Non Negative Matrix Factorization
- Latent Dirichlet allocation(LDA)
Neural Networks
- Self Organizing Map
- Perceptron
- Back-Propagation
- Hopfield Network
- Radial Basis Function Network(RBFN)
- Backpropagation
- Autoencoders
- Hopfield networks
- Boltzmann machines
- Restricted Boltzmann machines
- Spiking Neural Networks
- Leaning Vector Quantization(LVQ)
Others
- Support Vector Machines(SVM)
- Evolutionary Algorithms
- Inductive Logic Programming(ILP)
- Reinforcement Learning(Q-Learning, Temporal Difference, State-Action-Reward-State-Action(SARSA))
- ANOVA
- Information Fuzzy Netowkr(IFN)
- Page Rank
Based on https://cdn.datafloq.com/cms/2016/04/25/12-algorithms-every-data-scientist-should-know.jpg
