Classical machine learning and statistics datasets from the UCI Machine Learning Repository and other sources.

The datasets package defines two different kinds of datasets:

  • small data sets which are directly (or indirectly with file-embed) embedded in the package as pure values and do not require network or IO to download the data set. This includes Iris, Anscombe and OldFaithful.

  • other data sets which need to be fetched over the network with Numeric.Datasets.getDataset and are cached in a local temporary directory.

The datafiles/ directory of this package includes copies of a few famous datasets, such as Titanic, Nightingale and Michelson.

Example :

import Numeric.Datasets (getDataset)
import Numeric.Datasets.Iris (iris)
import Numeric.Datasets.Abalone (abalone)

main = do
  -- The Iris data set is embedded
  print (length iris)
  print (head iris)
  -- The Abalone dataset is fetched
  abas <- getDataset abalone
  print (length abas)
  print (head abas)

Changes

0.3 * ‘datasets’ hosted within the DataHaskell/dh-core project

* use 'req' for HTTP and HTTPS requests, instead of 'wreq'

* Mushroom and Titanic datasets

* Restructured top-level documentation

* Removed 'csvDatasetPreprocess' and added 'withPreprocess'. Now bytestring preprocessing is more compositional, i.e. 'withPreprocess' can be used with JSON datasets as well.

0.2.5

* Old Faithful matches R dataset

0.2.4

* Netflix dataset

0.2.3

* Coal dataset

* New internal API

* Ord instance for IrisClass

0.2.2

* Enum, Bounded instances for IrisClass

* Gapminder dataset

* Use wreq for HTTP and HTTPS requests

0.2.1

* Wine quality datasets

* Vocabulary, UN, States datasets

* CO2, Sunspots and Quakes datasets

0.2.0.3

* Further GHC portability

0.2.0.2

* Improve GHC portability

0.2.0.1

* Bugfix: include embedded data files in cabal extra-source-files

0.2

* iris dataset is a pure value (with file-embed)

* Michelson, Nightingale and BostonHousing datasets
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