# Version 1.2.0¶

In Development

## Legend for changelogs¶

• Major Feature : something big that you couldn’t do before.

• Feature : something that you couldn’t do before.

• Efficiency : an existing feature now may not require as much computation or memory.

• Enhancement : a miscellaneous minor improvement.

• Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.

• API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

## Changed models¶

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

## Changelog¶

### sklearn.datasets¶

• Enhancement Introduce the new parameter parser in datasets.fetch_openml. parser="pandas" allows to use the very CPU and memory efficient pandas.read_csv parser to load dense ARFF formatted dataset files. It is possible to pass parser="liac-arff" to use the old LIAC parser. When parser="auto", dense datasets are loaded with “pandas” and sparse datasets are loaded with “liac-arff”. Currently, parser="liac-arff" by default and will change to parser="auto" in version 1.4 #21938 by Guillaume Lemaitre.

### sklearn.metrics¶

• Feature class_likelihood_ratios is added to compute the positive and negative likelihood ratios derived from the confusion matrix of a binary classification problem. #22518 by Arturo Amor.

## Code and Documentation Contributors¶

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.1, including:

TODO: update at the time of the release.