Changelog for python2-scikit-learn-0.20.2-bp151.2.2.x86_64.rpm :

* Wed Jan 30 2019 Matej Cepl - Switch off tests, gh#scikit-learn/scikit-learn#12369
* Tue Jan 29 2019 mceplAATTsuse.com- Update to 0.20.2:
* This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0. Note that we also include some API changes in this release, so you might get some extra warnings after updating from 0.20.0.
* Wed Oct 24 2018 Dirk Mueller - update to 0.20.0:
* http://scikit-learn.org/stable/whats_new.html#version-0-20-0 Support for Python 3.3 has been officially dropped- drop scikit-learn-skip-test.patch (merged)
* Thu May 17 2018 tchvatalAATTsuse.com- Skip test sklearn.linear_model.tests.test_logistic.test_max_iter
* Upstream plans to fix it in next release
* scikit-learn-skip-test.patch
* Thu May 17 2018 tchvatalAATTsuse.com- Update package to properly state dependencies as in setup.py- Install license file
* Mon Oct 30 2017 arunAATTgmx.de- update to version 0.19.1:
* API changes + Reverted the addition of metrics.ndcg_score and metrics.dcg_score which had been merged into version 0.19.0 by error. The implementations were broken and undocumented. + return_train_score which was added to model_selection.GridSearchCV, model_selection.RandomizedSearchCV and model_selection.cross_validate in version 0.19.0 will be changing its default value from True to False in version 0.21. We found that calculating training score could have a great effect on cross validation runtime in some cases. Users should explicitly set return_train_score to False if prediction or scoring functions are slow, resulting in a deleterious effect on CV runtime, or to True if they wish to use the calculated scores. #9677 by Kumar Ashutosh and Joel Nothman. + correlation_models and regression_models from the legacy gaussian processes implementation have been belatedly deprecated. #9717 by Kumar Ashutosh.
* Bug fixes + Avoid integer overflows in metrics.matthews_corrcoef. #9693 by Sam Steingold. + Fix ValueError in preprocessing.LabelEncoder when using inverse_transform on unseen labels. #9816 by Charlie Newey. + Fixed a bug in the objective function for manifold.TSNE (both exact and with the Barnes-Hut approximation) when n_components >= 3. #9711 by AATTgoncalo-rodrigues. + Fix regression in model_selection.cross_val_predict where it raised an error with method=\'predict_proba\' for some probabilistic classifiers. #9641 by James Bourbeau. + Fixed a bug where datasets.make_classification modified its input weights. #9865 by Sachin Kelkar. + model_selection.StratifiedShuffleSplit now works with multioutput multiclass or multilabel data with more than 1000 columns. #9922 by Charlie Brummitt. + Fixed a bug with nested and conditional parameter setting, e.g. setting a pipeline step and its parameter at the same time. #9945 by Andreas Müller and Joel Nothman.
* Regressions in 0.19.0 fixed in 0.19.1: + Fixed a bug where parallelised prediction in random forests was not thread-safe and could (rarely) result in arbitrary errors. #9830 by Joel Nothman. + Fix regression in model_selection.cross_val_predict where it no longer accepted X as a list. #9600 by Rasul Kerimov. + Fixed handling of model_selection.cross_val_predict for binary classification with method=\'decision_function\'. #9593 by Reiichiro Nakano and core devs. + Fix regression in pipeline.Pipeline where it no longer accepted steps as a tuple. #9604 by Joris Van den Bossche. + Fix bug where n_iter was not properly deprecated, leaving n_iter unavailable for interim use in linear_model.SGDClassifier, linear_model.SGDRegressor, linear_model.PassiveAggressiveClassifier, linear_model.PassiveAggressiveRegressor and linear_model.Perceptron. #9558 by Andreas Müller. + Dataset fetchers make sure temporary files are closed before removing them, which caused errors on Windows. #9847 by Joan Massich. + Fixed a regression in manifold.TSNE where it no longer supported metrics other than ‘euclidean’ and ‘precomputed’. #9623 by Oli Blum.
* Enhancements + Our test suite and utils.estimator_checks.check_estimators can now be run without Nose installed. #9697 by Joan Massich. + To improve usability of version 0.19’s pipeline.Pipeline caching, memory now allows joblib.Memory instances. This make use of the new utils.validation.check_memory helper. #9584 by Kumar Ashutosh + Some fixes to examples: #9750, #9788, #9815 + Made a FutureWarning in SGD-based estimators less verbose. #9802 by Vrishank Bhardwaj.
* Sun Sep 24 2017 arunAATTgmx.de- update to version 0.19.0:
* Highlights + We are excited to release a number of great new features including neighbors.LocalOutlierFactor for anomaly detection, preprocessing.QuantileTransformer for robust feature transformation, and the multioutput.ClassifierChain meta-estimator to simply account for dependencies between classes in multilabel problems. We have some new algorithms in existing estimators, such as multiplicative update in decomposition.NMF and multinomial linear_model.LogisticRegression with L1 loss (use solver=\'saga\'). + Cross validation is now able to return the results from multiple metric evaluations. The new model_selection.cross_validate can return many scores on the test data as well as training set performance and timings, and we have extended the scoring and refit parameters for grid/randomized search to handle multiple metrics. + You can also learn faster. For instance, the new option to cache transformations in pipeline.Pipeline makes grid search over pipelines including slow transformations much more efficient. And you can predict faster: if you’re sure you know what you’re doing, you can turn off validating that the input is finite using config_context. + We’ve made some important fixes too. We’ve fixed a longstanding implementation error in metrics.average_precision_score, so please be cautious with prior results reported from that function. A number of errors in the manifold.TSNE implementation have been fixed, particularly in the default Barnes-Hut approximation. semi_supervised.LabelSpreading and semi_supervised.LabelPropagation have had substantial fixes. LabelPropagation was previously broken. LabelSpreading should now correctly respect its alpha parameter.
* 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. + cluster.KMeans with sparse X and initial centroids given (bug fix) + cross_decomposition.PLSRegression with scale=True (bug fix) + ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor where min_impurity_split is used (bug fix) + gradient boosting loss=\'quantile\' (bug fix) + ensemble.IsolationForest (bug fix) + feature_selection.SelectFdr (bug fix) + linear_model.RANSACRegressor (bug fix) + linear_model.LassoLars (bug fix) + linear_model.LassoLarsIC (bug fix) + manifold.TSNE (bug fix) + neighbors.NearestCentroid (bug fix) + semi_supervised.LabelSpreading (bug fix) + semi_supervised.LabelPropagation (bug fix) + tree based models where min_weight_fraction_leaf is used (enhancement)
* complete changelog at http://scikit-learn.org/stable/whats_new.html
* Sun Jun 11 2017 toddrme2178AATTgmail.com- Implement single-spec version- Update source URL- Update to version 0.18.1
* Large number of changes. See: https://github.com/scikit-learn/scikit-learn/blob/0.18.1/doc/whats_new.rst
* Mon Jan 11 2016 toddrme2178AATTgmail.com- Switch to proper package name: python-scikit-learn
* Fri Nov 20 2015 Angelos Tzotsos - Update to version 0.17