GRAPE
GRAPE is a regression API in Python environment
Description
GRAPE makes it easy to fit a regression model with hyperparameter optimization. It strings together the workflow of model fitting, hyperparameter tuning, and model diagnostics. (model interpretability coming soon!).
- Available Regression Methods
- Elastic Net (from sklearn)
- Random Forest (from sklearn)
- xgboost
- lightgbm
- Hyperparameter Optimization
- Grape Uses Hyperopt's tree parzen estimator
1 maintainer
Metadata
1 maintainer
Release files for grape-model 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| grape-model-0.0.3.tar.gz | 17.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| grape_model-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 41.4 kB
Release files / grape-model-0.0.3.tar.gz
| Download URL | grape-model-0.0.3.tar.gz |
|---|---|
| Size | 17.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191101 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3
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Release files / grape_model-0.0.3-py3-none-any.whl
| Download URL | grape_model-0.0.3-py3-none-any.whl |
|---|---|
| Size | 23.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191101 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.3
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