scikit-learn: machine learning in Python — scikit-learn 0.19.2 documentation

Classification

Identifying to which category an object belongs to.

Applications: Spam detection, Image recognition.
Algorithms:
SVM, nearest neighbors, random forest, …

Examples

Regression

Predicting a continuous-valued attribute associated with an object.

Applications: Drug response, Stock prices.
Algorithms:
SVR, ridge regression, Lasso, …

Examples

Clustering

Automatic grouping of similar objects into sets.

Applications: Customer segmentation, Grouping experiment outcomes
Algorithms:
k-Means, spectral clustering, mean-shift, …

Examples

Dimensionality reduction

Reducing the number of random variables to consider.

Applications: Visualization, Increased efficiency
Algorithms:
PCA, feature selection, non-negative matrix factorization.

Examples

Model selection

Comparing, validating and choosing parameters and models.

Goal: Improved accuracy via parameter tuning
Modules:
grid search, cross validation, metrics.

Examples

Preprocessing

Feature extraction and normalization.

Application: Transforming input data such as text for use with machine learning algorithms.
Modules:
preprocessing, feature extraction.

Examples