superduper_sklearn
superduper allows users to work with arbitrary sklearn estimators, with additional support for pre-, post-processing and input/ output data-types.
Installation
pip install superduper_sklearn
API
| Class | Description |
|---|---|
superduper_sklearn.model.SklearnTrainer |
A trainer for sklearn models. |
superduper_sklearn.model.Estimator |
Estimator model. |
Examples
Estimator
from superduper_sklearn import Estimator
from sklearn.svm import SVC
model = Estimator(
identifier='test',
object=SVC(),
)
Training Example
Read more about this here
1 maintainer
Metadata
1 maintainer
Release files for superduper-sklearn 0.10.0
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| superduper_sklearn-0.10.0.tar.gz | 13.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| superduper_sklearn-0.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.9 kB
Release files / superduper_sklearn-0.10.0.tar.gz
| Download URL | superduper_sklearn-0.10.0.tar.gz |
|---|---|
| Size | 13.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / superduper_sklearn-0.10.0-py3-none-any.whl
| Download URL | superduper_sklearn-0.10.0-py3-none-any.whl |
|---|---|
| Size | 11.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
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