Congratulations for all these improvements and for orchestrating the release !

On 11/08/2017 23:49, Olivier Grisel wrote:
Grab it with pip or conda !

Quoting the release highlights from the website:

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.

Please see the full changelog at:

Notably some models have changed behaviors (bug fixes) and some methods or parameters part of the public API have been deprecated.

A big thank you to anyone who made this release possible and Joel in particular.


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