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Update parquet files
Browse files- .gitattributes +0 -51
- README.md +0 -59
- imodels--credit-card/csv-test.parquet +3 -0
- imodels--credit-card/csv-train.parquet +3 -0
- test.csv +0 -0
- train.csv +0 -0
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# Audio files - uncompressed
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README.md
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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: credit-card
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size_categories:
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- 10K<n<100K
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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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- medicine
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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 credit-card dataset from UCI (link [here](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)). See details there and use carefully.
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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 `default.payment.next.month`.
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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/credit-card")
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df = pd.DataFrame(dataset['train'])
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X = df.drop(columns=['default.payment.next.month'])
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y = df['default.payment.next.month'].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=['default.payment.next.month'])
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y_test = df['default.payment.next.month'].values
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print('accuracy', np.mean(m.predict(X_test) == y_test))
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```
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imodels--credit-card/csv-test.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:1ce07a34d929a20ab77dbffa6a407253a76a1cc883c5f845cb1b762bf9be84bb
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size 438320
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imodels--credit-card/csv-train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:d69fd9ddb1a384a794ada87aeb4903a1f74d1580b017b5268300ccc60f02f190
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size 1728155
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test.csv
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train.csv
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