Details on Scikit-Learn Python based Machine Learning Library

SCiKit-Learn Python based Machine Learning Library which is open sources through BSD.



  • Python >= 2.6
  • numpy > = 1.3
  • scipy >= 0.7

Includes supervised learning algorithms:

  • Generalized Linear Model with with scipy.sparse bindings for wide features datasets
  • Support Vector Machine (SVM) based on libsvm
  • Stochastic Gradient Descent
  • bayesian methods
  • Gaussian Processes
  • Nearest Neighbors
  • Partial Least Squares
  • Naive Bayes
  • Decision Trees
  • Ensemble methods
  • Multiclass and multilabel algorithms
  • Feature selection
  • L1 and L1+L2 regularized regression methods aka Lasso and Elastic Net models implemented with algorithms such as LARS and coordinate descent
  • Linear and Quadratic Discriminant Analysis

Includes unsupervised clustering algorithms:

  • Gaussian mixture models
  • kmeans++
  • meanshift
  • affinity propagation
  • Manifold learning
  • spectral clustering
  • Decomposing signals in components (matrix factorization problems)
  • Covariance estimation
  • Novelty and Outlier Detection
  • Hidden Markov Models (HMMs)

Include other tools:

  • feature extractors for text content (token and char ngrams + hashing vectorizer)
  • univariate feature selections
  • a simple pipe line tool
  • numerous implementations of cross validation strategies
  • performance metrics evaluation and ploting (ROC curve, AUC, confusion matrix, …)
  • a grid search utility to perform hyper-parameters tuning using parallel cross validation
  • integration with joblib for caching partial results when working in interactive environment (e.g. using ipython)


  • Each algorithm implementation comes with sample programs demonstrating it’s usage either on toy data or real life datasets.

Source code:

  • Get the source code from Git-hub

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