API Reference — scikit-learn 0.20.4 documentation (original) (raw)

This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses. For reference on concepts repeated across the API, see Glossary of Common Terms and API Elements.

sklearn.base: Base classes and utility functions¶

Base classes for all estimators.

Functions¶

sklearn.cluster: Clustering¶

The sklearn.cluster module gathers popular unsupervised clustering algorithms.

User guide: See the Clustering section for further details.

Classes¶

Functions¶

sklearn.cluster.bicluster: Biclustering¶

Spectral biclustering algorithms.

Authors : Kemal Eren License: BSD 3 clause

User guide: See the Biclustering section for further details.

sklearn.covariance: Covariance Estimators¶

The sklearn.covariance module includes methods and algorithms to robustly estimate the covariance of features given a set of points. The precision matrix defined as the inverse of the covariance is also estimated. Covariance estimation is closely related to the theory of Gaussian Graphical Models.

User guide: See the Covariance estimation section for further details.

sklearn.datasets: Datasets¶

The sklearn.datasets module includes utilities to load datasets, including methods to load and fetch popular reference datasets. It also features some artificial data generators.

User guide: See the Dataset loading utilities section for further details.

Loaders¶

Samples generator¶

sklearn.decomposition: Matrix Decomposition¶

The sklearn.decomposition module includes matrix decomposition algorithms, including among others PCA, NMF or ICA. Most of the algorithms of this module can be regarded as dimensionality reduction techniques.

User guide: See the Decomposing signals in components (matrix factorization problems) section for further details.

sklearn.feature_selection: Feature Selection¶

The sklearn.feature_selection module implements feature selection algorithms. It currently includes univariate filter selection methods and the recursive feature elimination algorithm.

User guide: See the Feature selection section for further details.

sklearn.linear_model: Generalized Linear Models¶

The sklearn.linear_model module implements generalized linear models. It includes Ridge regression, Bayesian Regression, Lasso and Elastic Net estimators computed with Least Angle Regression and coordinate descent. It also implements Stochastic Gradient Descent related algorithms.

User guide: See the Generalized Linear Models section for further details.

sklearn.manifold: Manifold Learning¶

The sklearn.manifold module implements data embedding techniques.

User guide: See the Manifold learning section for further details.

sklearn.metrics: Metrics¶

See the Model evaluation: quantifying the quality of predictions section and the Pairwise metrics, Affinities and Kernels section of the user guide for further details.

The sklearn.metrics module includes score functions, performance metrics and pairwise metrics and distance computations.

Classification metrics¶

See the Classification metrics section of the user guide for further details.

Clustering metrics¶

See the Clustering performance evaluation section of the user guide for further details.

The sklearn.metrics.cluster submodule contains evaluation metrics for cluster analysis results. There are two forms of evaluation:

• supervised, which uses a ground truth class values for each sample.
• unsupervised, which does not and measures the ‘quality’ of the model itself.

Pairwise metrics¶

See the Pairwise metrics, Affinities and Kernels section of the user guide for further details.

sklearn.multiclass: Multiclass and multilabel classification¶

Multiclass and multilabel classification strategies¶

This module implements multiclass learning algorithms:

• one-vs-the-rest / one-vs-all
• one-vs-one
• error correcting output codes

The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. For example, it is possible to use these estimators to turn a binary classifier or a regressor into a multiclass classifier. It is also possible to use these estimators with multiclass estimators in the hope that their accuracy or runtime performance improves.

All classifiers in scikit-learn implement multiclass classification; you only need to use this module if you want to experiment with custom multiclass strategies.

The one-vs-the-rest meta-classifier also implements a predict_proba method, so long as such a method is implemented by the base classifier. This method returns probabilities of class membership in both the single label and multilabel case. Note that in the multilabel case, probabilities are the marginal probability that a given sample falls in the given class. As such, in the multilabel case the sum of these probabilities over all possible labels for a given sample will not sum to unity, as they do in the single label case.

User guide: See the Multiclass and multilabel algorithms section for further details.

sklearn.naive_bayes: Naive Bayes¶

The sklearn.naive_bayes module implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions.

User guide: See the Naive Bayes section for further details.

sklearn.pipeline: Pipeline¶

The sklearn.pipeline module implements utilities to build a composite estimator, as a chain of transforms and estimators.

sklearn.preprocessing: Preprocessing and Normalization¶

The sklearn.preprocessing module includes scaling, centering, normalization, binarization and imputation methods.

User guide: See the Preprocessing data section for further details.

sklearn.random_projection: Random projection¶

Random Projection transformers

Random Projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes.

The dimensions and distribution of Random Projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset.

The main theoretical result behind the efficiency of random projection is theJohnson-Lindenstrauss lemma (quoting Wikipedia):

In mathematics, the Johnson-Lindenstrauss lemma is a result concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space. The lemma states that a small set of points in a high-dimensional space can be embedded into a space of much lower dimension in such a way that distances between the points are nearly preserved. The map used for the embedding is at least Lipschitz, and can even be taken to be an orthogonal projection.

User guide: See the Random Projection section for further details.

sklearn.semi_supervised Semi-Supervised Learning¶

The sklearn.semi_supervised module implements semi-supervised learning algorithms. These algorithms utilized small amounts of labeled data and large amounts of unlabeled data for classification tasks. This module includes Label Propagation.

User guide: See the Semi-Supervised section for further details.

sklearn.svm: Support Vector Machines¶

The sklearn.svm module includes Support Vector Machine algorithms.

User guide: See the Support Vector Machines section for further details.

Estimators¶

sklearn.utils: Utilities¶

The sklearn.utils module includes various utilities.

Developer guide: See the Utilities for Developers page for further details.

Utilities from joblib:

Recently deprecated¶

To be removed in 0.23¶

To be removed in 0.22¶