The scikit-learn modules for different models

The scikit-learn library is organized into submodules. Each submodule contains algorithms and helper methods for a certain class of machine learning models and approaches.

Here is a sample of those submodules, including some example models:

Submodule	Description	Example models
cluster	This is the unsupervised clustering	KMeans and Ward
decomposition	This is the dimensionality reduction	PCA and NMF
ensemble	This involves ensemble-based methods	AdaBoostClassifier, AdaBoostRegressor, RandomForestClassifier, RandomForestRegressor
lda	This stands for latent discriminant analysis	LDA
linear_model	This is the generalized linear model	LinearRegression, LogisticRegression, Lasso and Perceptron
mixture	This is the mixture model	GMM and VBGMM
naive_bayes	This involves supervised learning based on Bayes' theorem	BaseNB and BernoulliNB, GaussianNB
neighbors	These are k-nearest neighbors	KNeighborsClassifier, KNeighborsRegressor...