https://github.com/gift-exe/multilabel-encoding
Multilabel Encoding. Found out about this concept while checking out Alexey Grigorev's free ML course. Decided to explore it (^_^ )
https://github.com/gift-exe/multilabel-encoding
classification encoding feature-extraction machine-learning multilabel-classification
Last synced: 7 months ago
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Multilabel Encoding. Found out about this concept while checking out Alexey Grigorev's free ML course. Decided to explore it (^_^ )
- Host: GitHub
- URL: https://github.com/gift-exe/multilabel-encoding
- Owner: gift-exe
- Created: 2022-09-15T07:12:02.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-22T20:43:19.000Z (about 1 year ago)
- Last Synced: 2025-01-14T00:31:13.979Z (9 months ago)
- Topics: classification, encoding, feature-extraction, machine-learning, multilabel-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 1.33 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Simple Tool For Encoding Multilabeled Columns
### How to use:
---
The `MultiLabelEncoder` class from `main.py`, takes care of encoding the multilabeled column.
To use, create a `MultiLabelEncoder` object, parameters are:
`multilabel_column` type: pd.Series
`unique_labels` type:set [optional]
`delimiter` type:str [optional]
As of 22.07.2024 the Encoder works best on csv files, where the columns are strings.
Typically looking like this:
|TAGS |
|-----------------------------------------|
| "Factory Tuner,Luxury,High-Performance" |
| "Luxury,Performance" |
| "Luxury,High-Performance" |
Here the delimiter is a comma ','
For sample usage, check out thte `mlc.ipynb` notebook
Thank you (^_^ )