scikit-learn-whiskers
A collection (only one at this time) of tools aimed to help with some tasks of machine learning and datascience studies.
These tools are intended to be compatible with scikit-learn utilities, and work properly inside a Pipeline.
WhiskerOutliers
A class to mark as outliers the values that can visually be identified as outliers from a typical box and whiskers plot.
This class implements .fit, transform and fit_transform, as well as get_params and set_params methods as any standard scikit-learn implementation.
StandardOutliers
A class to mark as outliers the values outside the range threshold * standard deviation around the mean.
This class implements .fit, transform and fit_transform, as well as get_params and set_params methods as any standard scikit-learn implementation.
Requisites:
NumPyPandasScikit-Learn
Installation
To install it: pip git+https://github.com/ayaranitram/scikit-learn-whiskers
Release files for scikit-learn-whiskers 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scikit-learn-whiskers-0.3.0.tar.gz | 5.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scikit_learn_whiskers-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.0 kB
Release files / scikit-learn-whiskers-0.3.0.tar.gz
| Download URL | scikit-learn-whiskers-0.3.0.tar.gz |
|---|---|
| Size | 5.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / scikit_learn_whiskers-0.3.0-py3-none-any.whl
| Download URL | scikit_learn_whiskers-0.3.0-py3-none-any.whl |
|---|---|
| Size | 8.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/4.0.2 CPython/3.10.8
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