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--- |
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annotations_creators: [] |
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language: [] |
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language_creators: [] |
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license: [] |
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multilinguality: [] |
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pretty_name: compas-recividivsm |
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size_categories: |
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- 1K<n<10K |
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source_datasets: [] |
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tags: |
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- interpretability |
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- fairness |
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task_categories: |
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- tabular-classification |
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task_ids: [] |
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--- |
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Port of the compas-recidivism dataset from propublica (github [here](https://github.com/propublica/compas-analysis)). See details there and use carefully, as there are serious known social impacts and biases present in this dataset. |
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Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb). |
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The target is the binary outcome `is_recid`. |
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### Sample usage |
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Load the data: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("imodels/compas-recidivism") |
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df = pd.DataFrame(dataset['train']) |
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X = df.drop(columns=['is_recid']) |
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y = df['is_recid'].values |
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``` |
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Fit a model: |
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``` |
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import imodels |
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import numpy as np |
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m = imodels.FIGSClassifier(max_rules=5) |
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m.fit(X, y) |
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print(m) |
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``` |
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Evaluate: |
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``` |
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df_test = pd.DataFrame(dataset['test']) |
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X_test = df.drop(columns=['is_recid']) |
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y_test = df['is_recid'].values |
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print('accuracy', np.mean(m.predict(X_test) == y_test)) |
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``` |
