머신러닝 알고리즘 Cheat Sheet


각종 머신러닝 알고리즘의 Cheat Sheet입니다! 매번 검색하기 번거로워 인터넷에 있는 자료들을 가지고 왔습니다

Dummies 자료

AlgorithmBest atProsCons
Random ForestApt at almost any machine learning problem
Bioinformatics
Can work in parallel
Seldom overfits
Automatically handles missing values
No need to transform any variable
No need to tweak parameters
Can be used by almost anyone with excellent results
Difficult to interpret
Weaker on regression when estimating values at the extremities of the distribution of response values
Biased in multiclass problems toward more frequent classes
Gradient BoostingApt at almost any machine learning problem
Search engines (solving the problem of learning to rank)
It can approximate most nonlinear function
Best in class predictor
Automatically handles missing values
No need to transform any variable
It can overfit if run for too many iterations
Sensitive to noisy data and outliers
Doesn’t work well without parameter tuning
Linear regressionBaseline predictions
Econometric predictions
Modelling marketing responses
Simple to understand and explain
It seldom overfits
Using L1 & L2 regularization is effective in feature selection
Fast to train
Easy to train on big data thanks to its stochastic version
You have to work hard to make it fit nonlinear functions
Can suffer from outliers
Support Vector MachinesCharacter recognition
Image recognition
Text classification
Automatic nonlinear feature creation
Can approximate complex nonlinear functions
Difficult to interpret when applying nonlinear kernels
Suffers from too many examples, after 10,000 examples it starts taking too long to train
K-nearest NeighborsComputer vision
Multilabel tagging
Recommender systems
Spell checking problems
Fast, lazy training
Can naturally handle extreme multiclass problems (like tagging text)
Slow and cumbersome in the predicting phase
Can fail to predict correctly due to the curse of dimensionality
AdaboostFace detectionAutomatically handles missing values
No need to transform any variable
It doesn’t overfit easily
Few parameters to tweak
It can leverage many different weak-learners
Sensitive to noisy data and outliers
Never the best in class predictions
Naive BayesFace recognition
Sentiment analysis
Spam detection
Text classification
Easy and fast to implement, doesn’t require too much memory and can be used for online learning
Easy to understand
Takes into account prior knowledge
Strong and unrealistic feature independence assumptions
Fails estimating rare occurrences
Suffers from irrelevant features
Neural NetworksImage recognition
Language recognition and translation
Speech recognition
Vision recognition
Can approximate any nonlinear function
Robust to outliers
Works only with a portion of the examples (the support vectors)
Very difficult to set up
Difficult to tune because of too many parameters and you have also to decide the architecture of the network
Difficult to interpret
Easy to overfit
Logistic regressionOrdering results by probability
Modelling marketing responses
Simple to understand and explain
It seldom overfits
Using L1 & L2 regularization is effective in feature selection
The best algorithm for predicting probabilities of an event
Fast to train
Easy to train on big data thanks to its stochastic version
You have to work hard to make it fit nonlinear functions
Can suffer from outliers
SVDRecommender systemsCan restructure data in a meaningful wayDifficult to understand why data has been restructured in a certain way
PCARemoving collinearity
Reducing dimensions of the dataset
Can reduce data dimensionalityImplies strong linear assumptions (components are a weighted summations of features)
K-meansSegmentationFast in finding clusters
Can detect outliers in multiple dimensions
Suffers from multicollinearity
Clusters are spherical, can’t detect groups of other shape
Unstable solutions, depends on initialization

Microsoft Azure Machine Learning 자료

Reference


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