# 0.11¶

## Changelog¶

- Merged dense and sparse implementations and added partial_fit (support for online/minibatch learning) and warm_start to the
Stochastic Gradient Descentmodule by Mathieu Blondel.- Dense and sparse implementations of
Support Vector Machinesclasses andlinear_model.LogisticRegressionmerged by Lars Buitinck.- Regressors can now be used as base estimator in the
Multiclass and multilabel algorithmsmodule by Mathieu Blondel.- Added Matthews correlation coefficient (
metrics.matthews_corrcoef) and added macro and micro average options tometrics.precision_score,metrics.recall_scoreandmetrics.f1_scoreby Satrajit Ghosh.- Added n_jobs option to
metrics.pairwise.pairwise_distancesandmetrics.pairwise.pairwise_kernelsfor parallel computation, by Mathieu Blondel.Out of Bag Estimatesof generalization error forEnsemble methodsby Andreas Müller.Randomized sparse models: Randomized sparse linear models for feature selection, by Alexandre Gramfort and Gael VaroquauxLabel Propagationfor semi-supervised learning, by Clay Woolam.Notethe semi-supervised API is still work in progress, and may change.- Added BIC/AIC model selection to classical
Gaussian mixture modelsand unified the API with the remainder of scikit-learn, by Bertrand ThirionK-meanscan now be run in parallel, using the n_jobs argument to eitherK-meansorKMeans, by Robert Layton.- Improved
Cross-ValidationandGrid Searchdocumentation and introduced the newcross_validation.train_test_splithelper function by Olivier Griselsvm.SVCmembers coef_ and intercept_ changed sign for consistency with decision_function; forkernel==linear, coef_ was fixed in the the one-vs-one case, by Andreas Müller.- Performance improvements to efficient leave-one-out cross-validated Ridge regression, esp. for the
n_samples > n_featurescase, inlinear_model.RidgeCV, by Reuben Fletcher-Costin.- Simplication of the
Text feature extractionAPI and fixed an issue with possible negative IDF, by Olivier Grisel.

## API changes summary¶

- NeighborsClassifier and NeighborsRegressor are gone in the module
Nearest Neighbors. Use the classesKNeighborsClassifier,RadiusNeighborsClassifier,KNeighborsRegressorand/orRadiusNeighborsRegressorinstead.- Sparse classes in the
Stochastic Gradient Descentmodule are now deprecated.- methods rvs and decode in
GMMmodule are now deprecated. sample and score or predict should be used instead.- attribute _scores and _pvalues in univariate feature selection objects are now deprecated. scores_ or pvalues_ should be used instead.
- In
LogisticRegression,LinearSVC,SVCandNuSVC, the class_weight parameter is now an initialization parameter, not a parameter to fit. This makes grid searches over this parameter possible.- LFW
datais now always shape(n_samples, n_features)to be consistent with the Olivetti faces dataset. Useimagesandpairsattribute to access the natural images shapes instead.- Setting scale_C=True by default in SVM and LogisticRegression models. This allows to have a regularization parameter independent of the number of samples. The scale_C parameter will disappear in v0.12.
- In
svm.LinearSVC, the meaning of the multi_class parameter changed. Options now are ‘ovr’ and ‘crammer_singer’, with ‘ovr’ being the default. This does not change the default behavior but hopefully is less confusing.- The preprocessor / analyzer nested structure for text feature extraction has been removed. All those features are now directly passed as flat constructor arguments to
feature_selection.text.Vectorizerandfeature_selection.text.CountVectorizer.- Class
feature_selection.text.Vectorizernow derives directly fromfeature_selection.text.CountVectorizerto make grid search trivial.

# 0.10¶

## Changelog¶

- Python 2.5 compatibility was dropped; the minimum Python version needed to use scikit-learn is now 2.6.
Sparse inverse covarianceestimation using the graph Lasso, with associated cross-validated estimator, by Gael Varoquaux- New
Treemodule by Brian Holt, Peter Prettenhofer, Satrajit Ghosh and Gilles Louppe. The module comes with complete documentation and examples.- Fixed a bug in the RFE module by Gilles Louppe (issue #378).
- Fixed a memory leak in in
Support Vector Machinesmodule by Brian Holt (issue #367).- Faster tests by Fabian Pedregosa and others.
- Silhouette Coefficient cluster analysis evaluation metric added as
sklearn.metrics.silhouette_scoreby Robert Layton.- Fixed a bug in
K-meansin the handling of then_initparameter: the clustering algorithm used to be runn_inittimes but the last solution was retained instead of the best solution by Olivier Grisel.- Minor refactoring in
Stochastic Gradient Descentmodule; consolidated dense and sparse predict methods; Enhanced test time performance by converting model paramters to fortran-style arrays after fitting (only multi-class).- Adjusted Mutual Information metric added as
sklearn.metrics.adjusted_mutual_info_scoreby Robert Layton.- Models like SVC/SVR/LinearSVC/LogisticRegression from libsvm/liblinear now support scaling of C regularization parameter by the number of samples by Alexandre Gramfort.
- New
Ensemble Methodsmodule by Gilles Louppe and Brian Holt. The module comes with the random forest algorithm and the extra-trees method, along with documentation and examples.Novelty and Outlier Detection: outlier and novelty detection, by Virgile Fritsch.Kernel Approximation: a transform implementing kernel approximation for fast SGD on non-linear kernels by Andreas Müller.- Fixed a bug due to atom swapping in
Orthogonal Matching Pursuit (OMP)by Vlad Niculae.Sparse coding with a precomputed dictionaryby Vlad Niculae.Mini Batch K-Meansperformance improvements by Olivier Grisel.K-meanssupport for sparse matrices by Mathieu Blondel.- Improved documentation for developers and for the
sklearn.utilsmodule, by Jake VanderPlas.- Vectorized 20newsgroups dataset loader (
sklearn.datasets.fetch_20newsgroups_vectorized) by Mathieu Blondel.Multiclass and multilabel algorithmsby Lars Buitinck.- Utilities for fast computation of mean and variance for sparse matrices by Mathieu Blondel.
- Make
sklearn.preprocessing.scaleandsklearn.preprocessing.Scalerwork on sparse matrices by Olivier Grisel- Feature importances using decision trees and/or forest of trees, by Gilles Louppe.
- Parallel implementation of forests of randomized trees by Gilles Louppe.
sklearn.cross_validation.ShuffleSplitcan subsample the train sets as well as the test sets by Olivier Grisel.- Errors in the build of the documentation fixed by Andreas Müller.

## API changes summary¶

Here are the code migration instructions when updgrading from scikit-learn version 0.9:

Some estimators that may overwrite their inputs to save memory previously had

overwrite_parameters; these have been replaced withcopy_parameters with exactly the opposite meaning.This particularly affects some of the estimators in

linear_model. The default behavior is still to copy everything passed in.The SVMlight dataset loader

sklearn.datasets.load_svmlight_fileno longer supports loading two files at once; useload_svmlight_filesinstead. Also, the (unused)buffer_mbparameter is gone.Sparse estimators in the

Stochastic Gradient Descentmodule use dense parameter vectorcoef_instead ofsparse_coef_. This significantly improves test time performance.The

Covariance estimationmodule now has a robust estimator of covariance, the Minimum Covariance Determinant estimator.Cluster evaluation metrics in

metrics.clusterhave been refactored but the changes are backwards compatible. They have been moved to themetrics.cluster.supervised, along withmetrics.cluster.unsupervisedwhich contains the Silhouette Coefficient.The

permutation_test_scorefunction now behaves the same way ascross_val_score(i.e. uses the mean score across the folds.)Cross Validation generators now use integer indices (

indices=True) by default instead of boolean masks. This make it more intuitive to use with sparse matrix data.The functions used for sparse coding,

sparse_encodeandsparse_encode_parallelhave been combined intosklearn.decomposition.sparse_encode, and the shapes of the arrays have been transposed for consistency with the matrix factorization setting, as opposed to the regression setting.Fixed an off-by-one error in the SVMlight/LibSVM file format handling; files generated using

sklearn.datasets.dump_svmlight_fileshould be re-generated. (They should continue to work, but accidentally had one extra column of zeros prepended.)

BaseDictionaryLearningclass replaced bySparseCodingMixin.

sklearn.utils.extmath.fast_svdhas been renamedsklearn.utils.extmath.randomized_svdand the default oversampling is now fixed to 10 additional random vectors instead of doubling the number of components to extract. The new behavior follows the reference paper.

## People¶

The following people contributed to scikit-learn since last release:

- 246 Andreas Müller
- 242 Olivier Grisel
- 220 Gilles Louppe
- 183 Brian Holt
- 166 Gael Varoquaux
- 144 Lars Buitinck
- 73 Vlad Niculae
- 65 Peter Prettenhofer
- 64 Fabian Pedregosa
- 60 Robert Layton
- 55 Mathieu Blondel
- 52 Jake Vanderplas
- 44 Noel Dawe
- 38 Alexandre Gramfort
- 24 Virgile Fritsch
- 23 Satrajit Ghosh
- 3 Jan Hendrik Metzen
- 3 Kenneth C. Arnold
- 3 Shiqiao Du
- 3 Tim Sheerman-Chase
- 3 Yaroslav Halchenko
- 2 Bala Subrahmanyam Varanasi
- 2 DraXus
- 2 Michael Eickenberg
- 1 Bogdan Trach
- 1 Félix-Antoine Fortin
- 1 Juan Manuel Caicedo Carvajal
- 1 Nelle Varoquaux
- 1 Nicolas Pinto
- 1 Tiziano Zito
- 1 Xinfan Meng

# 0.9¶

scikit-learn 0.9 was released on September 2011, three months after the 0.8
release and includes the new modules *Manifold learning*, *The Dirichlet Process*
as well as several new algorithms and documentation improvements.

This release also includes the dictionary-learning work developed by Vlad Niculae as part of the Google Summer of Code program.

## Changelog¶

- New
Manifold learningmodule by Jake Vanderplas and Fabian Pedregosa.- New
Dirichlet ProcessGaussian Mixture Model by Alexandre PassosNearest Neighborsmodule refactoring by Jake Vanderplas : general refactoring, support for sparse matrices in input, speed and documentation improvements. See the next section for a full list of API changes.- Improvements on the
Feature selectionmodule by Gilles Louppe : refactoring of the RFE classes, documentation rewrite, increased efficiency and minor API changes.Sparse Principal Components Analysis (SparsePCA and MiniBatchSparsePCA)by Vlad Niculae, Gael Varoquaux and Alexandre Gramfort- Printing an estimator now behaves independently of architectures and Python version thanks to Jean Kossaifi.
Loader for libsvm/svmlight formatby Mathieu Blondel and Lars Buitinck- Documentation improvements: thumbnails in
example galleryby Fabian Pedregosa.- Important bugfixes in
Support Vector Machinesmodule (segfaults, bad performance) by Fabian Pedregosa.- Added
Multinomial Naive BayesandBernoulli Naive Bayesby Lars Buitinck- Text feature extraction optimizations by Lars Buitinck
- Chi-Square feature selection (
feature_selection.univariate_selection.chi2) by Lars Buitinck.Sample generatorsmodule refactoring by Gilles LouppeMulticlass and multilabel algorithmsby Mathieu Blondel- Ball tree rewrite by Jake Vanderplas
- Implementation of
DBSCANalgorithm by Robert Layton- Kmeans predict and transform by Robert Layton
- Preprocessing module refactoring by Olivier Grisel
- Faster mean shift by Conrad Lee
- New
Bootstrapping cross-validation,Random permutations cross-validation a.k.a. Shuffle & Splitand various other improvements in cross validation schemes by Olivier Grisel and Gael Varoquaux- Adjusted Rand index and V-Measure clustering evaluation metrics by Olivier Grisel
- Added
Orthogonal Matching Pursuitby Vlad Niculae- Added 2D-patch extractor utilites in the
Feature extractionmodule by Vlad Niculae- Implementation of
linear_model.LassoLarsCV(cross-validated Lasso solver using the Lars algorithm) andlinear_model.LassoLarsIC(BIC/AIC model selection in Lars) by Gael Varoquaux and Alexandre Gramfort- Scalability improvements to
metrics.roc_curveby Olivier Hervieu- Distance helper functions
metrics.pairwise.pairwise_distancesandmetrics.pairwise.pairwise_kernelsby Robert LaytonMini-Batch K-Meansby Nelle Varoquaux and Peter Prettenhofer.Downloading datasets from the mldata.org repositoryutilities by Pietro Berkes.The Olivetti faces datasetby David Warde-Farley.

## API changes summary¶

Here are the code migration instructions when updgrading from scikit-learn version 0.8:

The

scikits.learnpackage was renamedsklearn. There is still ascikits.learnpackage alias for backward compatibility.Third-party projects with a dependency on scikit-learn 0.9+ should upgrade their codebase. For instance under Linux / MacOSX just run (make a backup first!):

find -name "*.py" | xargs sed -i 's/\bscikits.learn\b/sklearn/g'Estimators no longer accept model parameters as

fitarguments: instead all parameters must be only be passed as constructor arguments or using the now publicset_paramsmethod inhereted frombase.BaseEstimator.Some estimators can still accept keyword arguments on the

fitbut this is restricted to data-dependent values (e.g. a Gram matrix or an affinity matrix that are precomputed from theXdata matrix.The

cross_valpackage has been renamed tocross_validationalthough there is also across_valpackage alias in place for backward compatibility.Third-party projects with a dependency on scikit-learn 0.9+ should upgrade their codebase. For instance under Linux / MacOSX just run (make a backup first!):

find -name "*.py" | xargs sed -i 's/\bcross_val\b/cross_validation/g'The

score_funcargument of thesklearn.cross_validation.cross_val_scorefunction is now expected to accepty_testandy_predictedas only arguments for classification and regression tasks orX_testfor unsupervised estimators.

gammaparameter for support vector machine algorithms is set to1 / n_featuresby default, instead of1 / n_samples.The

sklearn.hmmhas been marked as orphaned: it will be removed from scikit-learn in version 0.11 unless someone steps up to contribute documentation, examples and fix lurking numerical stability issues.

sklearn.neighborshas been made into a submodule. The two previously available estimators,NeighborsClassifierandNeighborsRegressorhave been marked as deprecated. Their functionality has been divided among five new classes:NearestNeighborsfor unsupervised neighbors searches,KNeighborsClassifier&RadiusNeighborsClassifierfor supervised classification problems, andKNeighborsRegressor&RadiusNeighborsRegressorfor supervised regression problems.

sklearn.ball_tree.BallTreehas been moved tosklearn.neighbors.BallTree. Using the former will generate a warning.

sklearn.linear_model.LARS()and related classes (LassoLARS, LassoLARSCV, etc.) have been renamed tosklearn.linear_model.Lars().All distance metrics and kernels in

sklearn.metrics.pairwisenow have a Y parameter, which by default is None. If not given, the result is the distance (or kernel similarity) between each sample in Y. If given, the result is the pairwise distance (or kernel similarity) between samples in X to Y.

sklearn.metrics.pairwise.l1_distanceis now calledmanhattan_distance, and by default returns the pairwise distance. For the component wise distance, set the parametersum_over_featurestoFalse.

Backward compatibilty package aliases and other deprecated classes and functions will be removed in version 0.11.

## People¶

38 people contributed to this release.

- 387 Vlad Niculae
- 320 Olivier Grisel
- 192 Lars Buitinck
- 179 Gael Varoquaux
- 168 Fabian Pedregosa (INRIA, Parietal Team)
- 127 Jake Vanderplas
- 120 Mathieu Blondel
- 85 Alexandre Passos
- 67 Alexandre Gramfort
- 57 Peter Prettenhofer
- 56 Gilles Louppe
- 42 Robert Layton
- 38 Nelle Varoquaux
- 32 Jean Kossaifi
- 30 Conrad Lee
- 22 Pietro Berkes
- 18 andy
- 17 David Warde-Farley
- 12 Brian Holt
- 11 Robert
- 8 Amit Aides
- 8 Virgile Fritsch
- 7 Yaroslav Halchenko
- 6 Salvatore Masecchia
- 5 Paolo Losi
- 4 Vincent Schut
- 3 Alexis Metaireau
- 3 Bryan Silverthorn
- 3 Andreas Müller
- 2 Minwoo Jake Lee
- 1 Emmanuelle Gouillart
- 1 Keith Goodman
- 1 Lucas Wiman
- 1 Nicolas Pinto
- 1 Thouis (Ray) Jones
- 1 Tim Sheerman-Chase

# 0.8¶

scikit-learn 0.8 was released on May 2011, one month after the first
“international” scikit-learn coding sprint and is
marked by the inclusion of important modules: *Hierarchical clustering*,
*Partial Least Squares*, *Non-negative matrix factorization (NMF or NNMF)*, initial support for Python 3 and by important
enhacements and bug fixes.

## Changelog¶

Several new modules where introduced during this release:

- New
Hierarchical clusteringmodule by Vincent Michel, Bertrand Thirion, Alexandre Gramfort and Gael Varoquaux.Kernel PCAimplementation by Mathieu BlondelThe Labeled Faces in the Wild face recognition datasetby Olivier Grisel.- New
Partial Least Squaresmodule by Edouard Duchesnay.Non-negative matrix factorization (NMF or NNMF)module Vlad Niculae- Implementation of the
Oracle Approximating Shrinkagealgorithm by Virgile Fritsch in theCovariance estimationmodule.

Some other modules benefited from significant improvements or cleanups.

- Initial support for Python 3: builds and imports cleanly, some modules are usable while others have failing tests by Fabian Pedregosa.
decomposition.PCAis now usable from the Pipeline object by Olivier Grisel.- Guide
How to optimize for speedby Olivier Grisel.- Fixes for memory leaks in libsvm bindings, 64-bit safer BallTree by Lars Buitinck.
- bug and style fixing in
K-meansalgorithm by Jan Schlüter.- Add attribute coverged to Gaussian Mixture Models by Vincent Schut.
- Implement transform, predict_log_proba in
lda.LDAby Mathieu Blondel.- Refactoring in the
Support Vector Machinesmodule and bug fixes by Fabian Pedregosa, Gael Varoquaux and Amit Aides.- Refactored SGD module (removed code duplication, better variable naming), added interface for sample weight by Peter Prettenhofer.
- Wrapped BallTree with Cython by Thouis (Ray) Jones.
- Added function
svm.l1_min_cby Paolo Losi.- Typos, doc style, etc. by Yaroslav Halchenko, Gael Varoquaux, Olivier Grisel, Yann Malet, Nicolas Pinto, Lars Buitinck and Fabian Pedregosa.

## People¶

People that made this release possible preceeded by number of commits:

- 159 Olivier Grisel
- 96 Gael Varoquaux
- 96 Vlad Niculae
- 94 Fabian Pedregosa
- 36 Alexandre Gramfort
- 32 Paolo Losi
- 31 Edouard Duchesnay
- 30 Mathieu Blondel
- 25 Peter Prettenhofer
- 22 Nicolas Pinto
- 11 Virgile Fritsch
- 7 Lars Buitinck
- 6 Vincent Michel
- 5 Bertrand Thirion
- 4 Thouis (Ray) Jones
- 4 Vincent Schut
- 3 Jan Schlüter
- 2 Julien Miotte
- 2 Matthieu Perrot
- 2 Yann Malet
- 2 Yaroslav Halchenko
- 1 Amit Aides
- 1 Andreas Müller
- 1 Feth Arezki
- 1 Meng Xinfan

# 0.7¶

scikit-learn 0.7 was released in March 2011, roughly three months after the 0.6 release. This release is marked by the speed improvements in existing algorithms like k-Nearest Neighbors and K-Means algorithm and by the inclusion of an efficient algorithm for computing the Ridge Generalized Cross Validation solution. Unlike the preceding release, no new modules where added to this release.

## Changelog¶

- Performance improvements for Gaussian Mixture Model sampling [Jan Schlüter].
- Implementation of efficient leave-one-out cross-validated Ridge in
linear_model.RidgeCV[Mathieu Blondel]- Better handling of collinearity and early stopping in
linear_model.lars_path[Alexandre Gramfort and Fabian Pedregosa].- Fixes for liblinear ordering of labels and sign of coefficients [Dan Yamins, Paolo Losi, Mathieu Blondel and Fabian Pedregosa].
- Performance improvements for Nearest Neighbors algorithm in high-dimensional spaces [Fabian Pedregosa].
- Performance improvements for
cluster.KMeans[Gael Varoquaux and James Bergstra].- Sanity checks for SVM-based classes [Mathieu Blondel].
- Refactoring of
neighbors.NeighborsClassifierandneighbors.kneighbors_graph: added different algorithms for the k-Nearest Neighbor Search and implemented a more stable algorithm for finding barycenter weigths. Also added some developer documentation for this module, see notes_neighbors for more information [Fabian Pedregosa].- Documentation improvements: Added
pca.RandomizedPCAandlinear_model.LogisticRegressionto the class reference. Also added references of matrices used for clustering and other fixes [Gael Varoquaux, Fabian Pedregosa, Mathieu Blondel, Olivier Grisel, Virgile Fritsch , Emmanuelle Gouillart]- Binded decision_function in classes that make use of liblinear, dense and sparse variants, like
svm.LinearSVCorlinear_model.LogisticRegression[Fabian Pedregosa].- Performance and API improvements to
metrics.euclidean_distancesand topca.RandomizedPCA[James Bergstra].- Fix compilation issues under NetBSD [Kamel Ibn Hassen Derouiche]
- Allow input sequences of different lengths in
hmm.GaussianHMM[Ron Weiss].- Fix bug in affinity propagation caused by incorrect indexing [Xinfan Meng]

## People¶

People that made this release possible preceeded by number of commits:

- 85 Fabian Pedregosa
- 67 Mathieu Blondel
- 20 Alexandre Gramfort
- 19 James Bergstra
- 14 Dan Yamins
- 13 Olivier Grisel
- 12 Gael Varoquaux
- 4 Edouard Duchesnay
- 4 Ron Weiss
- 2 Satrajit Ghosh
- 2 Vincent Dubourg
- 1 Emmanuelle Gouillart
- 1 Kamel Ibn Hassen Derouiche
- 1 Paolo Losi
- 1 VirgileFritsch
- 1 Yaroslav Halchenko
- 1 Xinfan Meng

# 0.6¶

scikit-learn 0.6 was released on december 2010. It is marked by the inclusion of several new modules and a general renaming of old ones. It is also marked by the inclusion of new example, including applications to real-world datasets.

## Changelog¶

- New stochastic gradient descent module by Peter Prettenhofer. The module comes with complete documentation and examples.
- Improved svm module: memory consumption has been reduced by 50%, heuristic to automatically set class weights, possibility to assign weights to samples (see
SVM: Weighted samplesfor an example).- New
Gaussian Processesmodule by Vincent Dubourg. This module also has great documentation and some very neat examples. SeeGaussian Processes regression: basic introductory exampleorGaussian Processes classification example: exploiting the probabilistic outputfor a taste of what can be done.- It is now possible to use liblinear’s Multi-class SVC (option multi_class in
svm.LinearSVC)- New features and performance improvements of text feature extraction.
- Improved sparse matrix support, both in main classes (
grid_search.GridSearchCV) as in modules sklearn.svm.sparse and sklearn.linear_model.sparse.- Lots of cool new examples and a new section that uses real-world datasets was created. These include:
Faces recognition example using eigenfaces and SVMs,Species distribution modeling,Libsvm GUI,Wikipedia princial eigenvectorand others.- Faster
Least Angle Regressionalgorithm. It is now 2x faster than the R version on worst case and up to 10x times faster on some cases.- Faster coordinate descent algorithm. In particular, the full path version of lasso (
linear_model.lasso_path) is more than 200x times faster than before.- It is now possible to get probability estimates from a
linear_model.LogisticRegressionmodel.- module renaming: the glm module has been renamed to linear_model, the gmm module has been included into the more general mixture model and the sgd module has been included in linear_model.
- Lots of bug fixes and documentation improvements.

## People¶

People that made this release possible preceeded by number of commits:

- 207 Olivier Grisel
- 167 Fabian Pedregosa
- 97 Peter Prettenhofer
- 68 Alexandre Gramfort
- 59 Mathieu Blondel
- 55 Gael Varoquaux
- 33 Vincent Dubourg
- 21 Ron Weiss
- 9 Bertrand Thirion
- 3 Alexandre Passos
- 3 Anne-Laure Fouque
- 2 Ronan Amicel
- 1 Christian Osendorfer

# 0.5¶

## Changelog¶

### New classes¶

- Support for sparse matrices in some classifiers of modules
svmandlinear_model(seesvm.sparse.SVC,svm.sparse.SVR,svm.sparse.LinearSVC,linear_model.sparse.Lasso,linear_model.sparse.ElasticNet)- New
pipeline.Pipelineobject to compose different estimators.- Recursive Feature Elimination routines in module
Feature selection.- Addition of various classes capable of cross validation in the linear_model module (
linear_model.LassoCV,linear_model.ElasticNetCV, etc.).- New, more efficient LARS algorithm implementation. The Lasso variant of the algorithm is also implemented. See
linear_model.lars_path,linear_model.Larsandlinear_model.LassoLars.- New Hidden Markov Models module (see classes
hmm.GaussianHMM,hmm.MultinomialHMM,hmm.GMMHMM)- New module feature_extraction (see
class reference)- New FastICA algorithm in module sklearn.fastica

### Documentation¶

- Improved documentation for many modules, now separating narrative documentation from the class reference. As an example, see documentation for the SVM module and the complete class reference.

### Fixes¶

- API changes: adhere variable names to PEP-8, give more meaningful names.
- Fixes for svm module to run on a shared memory context (multiprocessing).
- It is again possible to generate latex (and thus PDF) from the sphinx docs.

### Examples¶

- new examples using some of the mlcomp datasets:
Classification of text documents: using a MLComp dataset,Classification of text documents using sparse features- Many more examaples. See here the full list of examples.

### External dependencies¶

- Joblib is now a dependencie of this package, although it is shipped with (sklearn.externals.joblib).

### Removed modules¶

- Module ann (Artificial Neural Networks) has been removed from the distribution. Users wanting this sort of algorithms should take a look into pybrain.

### Misc¶

- New sphinx theme for the web page.

## Authors¶

The following is a list of authors for this release, preceeded by number of commits:

- 262 Fabian Pedregosa
- 240 Gael Varoquaux
- 149 Alexandre Gramfort
- 116 Olivier Grisel
- 40 Vincent Michel
- 38 Ron Weiss
- 23 Matthieu Perrot
- 10 Bertrand Thirion
- 7 Yaroslav Halchenko
- 9 VirgileFritsch
- 6 Edouard Duchesnay
- 4 Mathieu Blondel
- 1 Ariel Rokem
- 1 Matthieu Brucher

# 0.4¶

## Changelog¶

Major changes in this release include:

- Coordinate Descent algorithm (Lasso, ElasticNet) refactoring & speed improvements (roughly 100x times faster).
- Coordinate Descent Refactoring (and bug fixing) for consistency with R’s package GLMNET.
- New metrics module.
- New GMM module contributed by Ron Weiss.
- Implementation of the LARS algorithm (without Lasso variant for now).
- feature_selection module redesign.
- Migration to GIT as content management system.
- Removal of obsolete attrselect module.
- Rename of private compiled extensions (aded underscore).
- Removal of legacy unmaintained code.
- Documentation improvements (both docstring and rst).
- Improvement of the build system to (optionally) link with MKL. Also, provide a lite BLAS implementation in case no system-wide BLAS is found.
- Lots of new examples.
- Many, many bug fixes ...

## Authors¶

The committer list for this release is the following (preceded by number of commits):

- 143 Fabian Pedregosa
- 35 Alexandre Gramfort
- 34 Olivier Grisel
- 11 Gael Varoquaux
- 5 Yaroslav Halchenko
- 2 Vincent Michel
- 1 Chris Filo Gorgolewski