pkgsrc/math/py-scikit-learn/distinfo

$NetBSD: distinfo,v 1.5 2018/10/02 16:53:46 minskim Exp $

SHA1 (scikit-learn-0.20.0.tar.gz) = abc1d6ff7f2a682183a01fba664eb931efaebdfc
RMD160 (scikit-learn-0.20.0.tar.gz) = d7fea3a02266d5080e495466b4e2351e46b426ad
SHA512 (scikit-learn-0.20.0.tar.gz) = f01fb0846e6e778fe6ab575f420143f04c6d35ac860b1b5e6f2a3a1a3be3e8e7ca7e20ec0995eeea80194d18c104f6cb776aadfd9c75cd3fca55b0926faee9c9
Size (scikit-learn-0.20.0.tar.gz) = 28061776 bytes
math/py-scikit-learn: Update to 0.20.0 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. 2018-10-02 18:53:46 +02:00			$NetBSD: distinfo,v 1.5 2018/10/02 16:53:46 minskim Exp $
Import py-scikit-learn-0.18.1 from pkgsrc as math/py-scikit-learn Packaged by Filip Hajny and updated by Kamel Derouiche and me. scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit scientific Python world (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems, accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering. 2017-07-05 23:31:28 +02:00
math/py-scikit-learn: Update to 0.20.0 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. 2018-10-02 18:53:46 +02:00			`SHA1 (scikit-learn-0.20.0.tar.gz) = abc1d6ff7f2a682183a01fba664eb931efaebdfc`
			`RMD160 (scikit-learn-0.20.0.tar.gz) = d7fea3a02266d5080e495466b4e2351e46b426ad`
			`SHA512 (scikit-learn-0.20.0.tar.gz) = f01fb0846e6e778fe6ab575f420143f04c6d35ac860b1b5e6f2a3a1a3be3e8e7ca7e20ec0995eeea80194d18c104f6cb776aadfd9c75cd3fca55b0926faee9c9`
			`Size (scikit-learn-0.20.0.tar.gz) = 28061776 bytes`