Metadata-Version: 2.1
Name: classWeightLearn
Version: 1.0.0
Summary: Optuna Class Weight Cost-Sensitive Learning
Home-page: 
Author: Mahayasa Adiputra
Author-email: mahayasa.a@kkumail.com
Description-Content-Type: text/markdown
Requires-Dist: scikit-learn (==1.6.1)
Requires-Dist: sdv (==1.18.0)
Requires-Dist: pandas (==2.0.3)
Requires-Dist: optuna (==4.2.0)

### 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<br>

### Instalation
```bash
pip install classWeightLearn
```
### Example Usage
```bash
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)
