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Changelog

v0.5.1 -- September 28, 2025 -- Maintenance release

Set minimum scikit-learn version to 1.6.0. Function check_estimator requires usage of validate_data which was introduced in 1.6.0

v0.5.0 -- September 8, 2025 -- Maintenance release

  • Python 3.9+

  • Testing: pytest and pytest-cov

  • CI: GitHub Actions

  • Scikit-learn API:

    • Fixed _set_params method which was not resetting individual estimators in the estimators collection

  • Functional API

    • Fixed saving OOF arrays in file

v0.4.0 -- August 12, 2019

Since v0.4.0 vecstack provides official support for Python 3.5 and higher only,
but still there is unofficial support for Python 2.7 and Python 3.4.
Please see details.

Scikit-learn API:

  • Fixed #31. sklearn.externals.six deprecation
  • Fixed #29. Out-of-memory in np.random.choice for very large ranges

Functional API:

  • Feature #18. Added support for N-dimensional input. Useful for convolutional nets.
  • Added aliases for mode parameter values which correspond to respective variant parameter values of StackingTransformer:
    • 'oof_pred_bag' == 'A'
    • 'oof_pred' == 'B'

v0.3.0 -- April 6, 2018

Introducing Scikit-learn API: StackingTransformer

  • Standard transformer class with fit and transform methods
  • Compatible with Pipeline and FeatureUnion

v0.2.2 -- February 23, 2018

  • Fixed #5. Wrong behavior during sparse matrix processing
  • Improved input data validation
  • Improved sparse matrix processing

v0.2.1 -- January 24, 2018 -- Maintenance release

  • Minor modifications

v0.2 -- January 23, 2018

New features:

  • Classification with probabilities
  • Modes: compute only what you need (only OOF, only predictions, both, etc.)
  • Save resulting arrays and log with model parameters

v0.1 -- November 22, 2016 -- Initial release

Features:

  • Functional stacking API
  • Regression
  • Classification with class labels
  • Ordinary and stratified k-fold split
  • User-defined metric
  • User-defined transformations for target and prediction