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
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