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Deep Learning

scikit-learn 1.1 Released

May 16, 2022
120 min read
blog-scikit-learn-1.1.0.png

scikit-learn 1.1 Now Available

scikit-learn is an open source machine learning library that supports supervised and unsupervised learning, and is used by an estimated 80% of data scientists, according to a recent Kaggle survey. 

The library contains implementations of many common ML algorithms and models, including the widely-used linear regression, decision tree, and gradient-boosting algorithms. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.

Highlights include:

  • Quantile loss in ensemble.HistGradientBoostingRegressor
  • get_feature_names_out Available in all Transformers
  • Grouping infrequent categories in OneHotEncoder
  • Performance improvements
  • MiniBatchNMF: an online version of NMF
  • BisectingKMeans: divide and cluster

For more details on the main highlights of the release, please refer to Release Highlights for scikit-learn 1.1.

To install the latest version (with pip):

pip install --upgrade scikit-learn

or with conda:

conda install -c conda-forge scikit-learn

Version 1.1.0

For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.1.

Legend for changelogs

  • Major Feature : something big that you couldn’t do before.
  • Feature : something that you couldn’t do before.
  • Efficiency : an existing feature now may not require as much computation or memory.
  • Enhancement : a miscellaneous minor improvement.
  • Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
  • API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

Minimal dependencies

Version 1.1.0 of scikit-learn requires python 3.8+, numpy 1.17.3+ and scipy 1.3.2+. Optional minimal dependency is matplotlib 3.1.2+.

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

Changelog

sklearn.calibration

    sklearn.cluster

      sklearn.compose

        sklearn.covariance

        sklearn.cross_decomposition

          sklearn.datasets

            sklearn.decomposition

            sklearn.discriminant_analysis

              sklearn.dummy

                sklearn.ensemble

                  sklearn.feature_extraction

                    sklearn.feature_selection

                    sklearn.gaussian_process

                    sklearn.impute

                      sklearn.inspection

                      sklearn.isotonic

                        sklearn.kernel_approximation

                          sklearn.linear_model

                            sklearn.manifold

                              sklearn.metrics

                                sklearn.mixture

                                  sklearn.model_selection

                                    sklearn.multiclass

                                      sklearn.neighbors

                                        sklearn.neural_network

                                          sklearn.pipeline

                                            sklearn.preprocessing

                                            sklearn.random_projection

                                              sklearn.svm

                                                sklearn.tree

                                                  sklearn.utils


                                                    Have any questions?
                                                    Contact Exxact Today


                                                    Free Resources

                                                    Browse our whitepapers, e-books, case studies, and reference architecture.

                                                    Explore
                                                    blog-scikit-learn-1.1.0.png
                                                    Deep Learning

                                                    scikit-learn 1.1 Released

                                                    May 16, 2022120 min read

                                                    scikit-learn 1.1 Now Available

                                                    scikit-learn is an open source machine learning library that supports supervised and unsupervised learning, and is used by an estimated 80% of data scientists, according to a recent Kaggle survey. 

                                                    The library contains implementations of many common ML algorithms and models, including the widely-used linear regression, decision tree, and gradient-boosting algorithms. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities.

                                                    Highlights include:

                                                    • Quantile loss in ensemble.HistGradientBoostingRegressor
                                                    • get_feature_names_out Available in all Transformers
                                                    • Grouping infrequent categories in OneHotEncoder
                                                    • Performance improvements
                                                    • MiniBatchNMF: an online version of NMF
                                                    • BisectingKMeans: divide and cluster

                                                    For more details on the main highlights of the release, please refer to Release Highlights for scikit-learn 1.1.

                                                    To install the latest version (with pip):

                                                    pip install --upgrade scikit-learn
                                                    

                                                    or with conda:

                                                    conda install -c conda-forge scikit-learn

                                                    Version 1.1.0

                                                    For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.1.

                                                    Legend for changelogs

                                                    • Major Feature : something big that you couldn’t do before.
                                                    • Feature : something that you couldn’t do before.
                                                    • Efficiency : an existing feature now may not require as much computation or memory.
                                                    • Enhancement : a miscellaneous minor improvement.
                                                    • Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
                                                    • API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

                                                    Minimal dependencies

                                                    Version 1.1.0 of scikit-learn requires python 3.8+, numpy 1.17.3+ and scipy 1.3.2+. Optional minimal dependency is matplotlib 3.1.2+.

                                                    Changed models

                                                    The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

                                                    Changelog

                                                    sklearn.calibration

                                                      sklearn.cluster

                                                        sklearn.compose

                                                          sklearn.covariance

                                                          sklearn.cross_decomposition

                                                            sklearn.datasets

                                                              sklearn.decomposition

                                                              sklearn.discriminant_analysis

                                                                sklearn.dummy

                                                                  sklearn.ensemble

                                                                    sklearn.feature_extraction

                                                                      sklearn.feature_selection

                                                                      sklearn.gaussian_process

                                                                      sklearn.impute

                                                                        sklearn.inspection

                                                                        sklearn.isotonic

                                                                          sklearn.kernel_approximation

                                                                            sklearn.linear_model

                                                                              sklearn.manifold

                                                                                sklearn.metrics

                                                                                  sklearn.mixture

                                                                                    sklearn.model_selection

                                                                                      sklearn.multiclass

                                                                                        sklearn.neighbors

                                                                                          sklearn.neural_network

                                                                                            sklearn.pipeline

                                                                                              sklearn.preprocessing

                                                                                              sklearn.random_projection

                                                                                                sklearn.svm

                                                                                                  sklearn.tree

                                                                                                    sklearn.utils


                                                                                                      Have any questions?
                                                                                                      Contact Exxact Today


                                                                                                      Free Resources

                                                                                                      Browse our whitepapers, e-books, case studies, and reference architecture.

                                                                                                      Explore