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... |