bc1e7deaab
Highlights: Missing values in features, represented by NaNs, are now accepted in column-wise preprocessing such as scalers. Each feature is fitted disregarding NaNs, and data containing NaNs can be transformed. The new impute module provides estimators for learning despite missing data. ColumnTransformer handles the case where different features or columns of a pandas.DataFrame need different preprocessing. String or pandas Categorical columns can now be encoded with OneHotEncoder or OrdinalEncoder. TransformedTargetRegressor helps when the regression target needs to be transformed to be modeled. PowerTransformer and KBinsDiscretizer join QuantileTransformer as non-linear transformations. Added sample_weight support to several estimators (including KMeans, BayesianRidge and KernelDensity) and improved stopping criteria in others (including MLPRegressor, GradientBoostingRegressor and SGDRegressor). This release is also the first to be accompanied by a Glossary of Common Terms and API Elements.
6 lines
433 B
Text
6 lines
433 B
Text
$NetBSD: distinfo,v 1.5 2018/10/02 16:53:46 minskim Exp $
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SHA1 (scikit-learn-0.20.0.tar.gz) = abc1d6ff7f2a682183a01fba664eb931efaebdfc
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RMD160 (scikit-learn-0.20.0.tar.gz) = d7fea3a02266d5080e495466b4e2351e46b426ad
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SHA512 (scikit-learn-0.20.0.tar.gz) = f01fb0846e6e778fe6ab575f420143f04c6d35ac860b1b5e6f2a3a1a3be3e8e7ca7e20ec0995eeea80194d18c104f6cb776aadfd9c75cd3fca55b0926faee9c9
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Size (scikit-learn-0.20.0.tar.gz) = 28061776 bytes
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