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math/pyscikitlearn,
Machine learning algorithms for Python
Branch: CURRENT,
Version: 0.20.0,
Package name: py27scikitlearn0.20.0,
Maintainer: fhajnyscikitlearn is a Python module integrating classic machine learning
algorithms in the tightlyknit 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: machinelearning as a versatile tool for science and
engineering.
Required to run:[
lang/g95] [
math/lapack] [
math/blas] [
devel/pysetuptools] [
math/pyscipy] [
math/pynumpy] [
lang/python27]
Required to build:[
devel/pynose] [
pkgtools/cwrappers]
Master sites:
SHA1: abc1d6ff7f2a682183a01fba664eb931efaebdfc
RMD160: d7fea3a02266d5080e495466b4e2351e46b426ad
Filesize: 27404.078 KB
Version history: (Expand)
 (20181002) Updated to version: py27scikitlearn0.20.0
 (20180806) Updated to version: py27scikitlearn0.19.2
 (20180310) Updated to version: py27scikitlearn0.19.1nb1
 (20171122) Updated to version: py27scikitlearn0.19.1
 (20171115) Updated to version: py27scikitlearn0.18.2
 (20170706) Package added to pkgsrc.se, version py27scikitlearn0.18.1 (created)
CVS history: (Expand)
20181215 22:12:25 by Thomas Klausner  Files touched by this commit (67)  
Log message:
*: update email for fhajny

20181002 18:53:46 by Min Sik Kim  Files touched by this commit (3)  
Log message:
math/pyscikitlearn: Update to 0.20.0
Highlights:
Missing values in features, represented by NaNs, are now accepted in
columnwise 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 nonlinear 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.

20180806 18:18:12 by Min Sik Kim  Files touched by this commit (2)  
Log message:
math/pyscikitlearn: Update to 0.19.2
This release is exclusively in order to support Python 3.7.

20180308 20:39:18 by Min Sik Kim  Files touched by this commit (1)  
Log message:
Remove dependencies unused if the Accelerate framework exists
Bump PKGREVISION.

20171121 19:45:29 by Min Sik Kim  Files touched by this commit (3)  
Log message:
math/pyscikitlearn: Update to 0.19.1
Notable new features since 0.18.2:
 `neighbors.LocalOutlierFactor` for anomaly detection
 `preprocessing.QuantileTransformer` for robust feature transformation
 `multioutput.ClassifierChain` metaestimator to simply account
for dependencies between classes in multilabel problem
 multiplicative update in `decomposition.NMF`
 multinomial `linear_model.LogisticRegression` with L1 loss

20171114 23:56:37 by Min Sik Kim  Files touched by this commit (2)  
Log message:
math/pyscikitlearn: Update to 0.18.2
Changes:
 Fixes for compatibility with NumPy 1.13.0
 Minor compatibility changes in the examples

20170705 23:31:28 by Min Sik Kim  Files touched by this commit (4)  
Log message:
Import pyscikitlearn0.18.1 from pkgsrc as math/pyscikitlearn
Packaged by Filip Hajny and updated by Kamel Derouiche and me.
scikitlearn is a Python module integrating classic machine learning
algorithms in the tightlyknit 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: machinelearning as a versatile tool for science and
engineering.
