semvec/pypi-packages huggingface dataset:
fastclf 0.1.0
fastclf is a package that makes it easy to test different models on different datasets.
License: MIT
Requires Python: >=3.5

# sklearnfast

### Description

fastclf is a python package that allows you to test your scikit-learn models (classifier) with a single line of code. It is designed to be used in Jupyter Notebooks and is inspired by the [fastai](https://github.com/fastai/fastai) library.



### Installation

```bash
pip install fastclf
```

### Usage

```python
from fastclf.modelinfo import classification_info
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from pprint import pprint
import pandas as pd

data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target

model = RandomForestClassifier()
(model, info) = classification_info(model, df, 'target')
pprint(info)

# {'f1': 0.972972972972973,
#  'accuracy': 0.9649122807017544,
#  'precision': 0.9722222222222222,
#  'recall': 0.9736842105263158}
```

### Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

### License

[MIT](https://choosealicense.com/licenses/mit/)