Cost Sensitive Learning
This is an Extended research from CTGAN-ENN, on algorithm-level approach by fine tuning class-weight using optuna, the supported model are SVM, Random Forest and LightBGM (other models come soon), the classifier model will run with the best class_weight parameter
Instalation
pip install classWeightLearn
Example Usage
from classWeightLearn import find_best_class_weight
best_weight_svc = find_best_class_weight(X, y, model_name='svc', n_trials=1)
classifier = SVC(class_weight=best_weight_svc)
Variables
- X : all features of your dataset
- y : target label of your dataset
- model_name : supported models are (svc, lightgbm and random_forest)
- n_trials : the number of trial that optuna will run (default:1)
1 maintainer
Metadata
1 maintainer
Release files for classWeightLearn 1.0.0
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| File | Size | Uploaded | Provenance | Actions |
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| File | Interpreter | ABI | Platform | Actions Reset |
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| classWeightLearn-1.0.0-py3-none-any.whl | Python 3 | none | any | Details for classWeightLearn-1.0.0-py3-none-any.whl |
Total release size: 4.8 kB
Release files / classWeightLearn-1.0.0.tar.gz
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| Tags | Python 3 |
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