readme: ---
  license: mit
  tags:
  - tabular-classification
  - sklearn
  - imodels
  datasets:
  - imodels/compas-recidivism
  widget:
    structuredData:
      age:
        - 40.0
        - 25.0
        - 36.0
        - 23.0
        - 29.0
      priors_count:
        - 0.0
        - 1.0
        - 11.0
        - 1.0
        - 0.0
      days_b_screening_arrest:
        - -1.0
        - -1.0
        - -1.0
        - -1.0
        - 0.0
      c_jail_time:
        - 0.0
        - 1.0
        - 2.0
        - 0.0
        - -1.0
      juv_fel_count:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      juv_other_count:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      juv_misd_count:
        - 0.0
        - 0.0
        - 0.0
        - 1.0
        - 0.0
      c_charge_degree:F:
        - 0.0
        - 1.0
        - 0.0
        - 0.0
        - 0.0
      c_charge_degree:M:
        - 1.0
        - 0.0
        - 1.0
        - 1.0
        - 1.0
      race:African-American:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      race:Asian:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      race:Caucasian:
        - 1.0
        - 0.0
        - 1.0
        - 1.0
        - 1.0
      race:Hispanic:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      race:Native_American:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      race:Other:
        - 0.0
        - 1.0
        - 0.0
        - 0.0
        - 0.0
      age_cat:25_-_45:
        - 1.0
        - 1.0
        - 1.0
        - 0.0
        - 1.0
      age_cat:Greater_than_45:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      age_cat:Less_than_25:
        - 0.0
        - 0.0
        - 0.0
        - 1.0
        - 0.0
      sex:Female:
        - 0.0
        - 0.0
        - 0.0
        - 0.0
        - 0.0
      sex:Male:
        - 1.0
        - 1.0
        - 1.0
        - 1.0
        - 1.0
  ---
  
  
  ### Load the data
  
  ```python
  from datasets import load_dataset
  import imodels
  import numpy as np
  from sklearn.model_selection import GridSearchCV
  import joblib
  
  dataset = load_dataset("imodels/compas-recidivism")
  df = pd.DataFrame(dataset['train'])
  X_train = df.drop(columns=['is_recid'])
  y_train = df['is_recid'].values
  
  df_test = pd.DataFrame(dataset['test'])
  X_test = df.drop(columns=['is_recid'])
  y_test = df['is_recid'].values
  ```
  
  ### Load the model
  
  ```python
  from huggingface_hub import hf_hub_url, cached_download
  import joblib
  import pandas as pd
  
  REPO_ID = "imodels/figs-compas-recidivism"
  FILENAME = "sklearn_model.joblib"
  
  model = joblib.load(cached_download(
      hf_hub_url(REPO_ID, FILENAME)
  ))
  
  # model is a `imodels.FIGSClassifier`
  ```
  
  ### Make prediction
  
  ```
  preds = model.predict(X_test)
  print('accuracy', np.mean(preds==y_test))
  # accuracy 0.6759165485112416
  ```
  