Project Title
This is a package that takes in X and y as training data and predicts two outputs as y1 and y2 for any given test data. This package removes the limitation of just have one target feature in machine learning.
Contributing
Contributions are always welcome!
The package needs to be extend to predict non-categorical variables.
Please adhere to this project's code of conduct.
Installation
To install this package run
pip install multilabeler
Feedback
If you have any feedback, please reach out to me at owodunniabraham@gmail.com
License
Usage/Examples
from multilabeler import BilableClassifier
model = BilableClassifier(xgboost())
1 maintainer
Metadata
1 maintainer
Release files for multilabeler 0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| multilabeler-0.4.tar.gz | 1.7 kB | Details |
Release files / multilabeler-0.4.tar.gz
| Download URL | multilabeler-0.4.tar.gz |
|---|---|
| Size | 1.7 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
a505b0118688a018b8a0a812e9ce1dbabaf01e49cfead1ec3ec373f826c4076b
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BLAKE2b-256 checksum How to use checksums |
6ccc5d1964b619819c7f22c91aeafb1e055157890c3cf9968c97f270b7f10c96
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twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
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