---
license: mit
---

---
tags:
- tabular-classification
- sklearn
datasets:
- wine-quality
- imodels/compas-recidivism
---


### 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
## Wine Quality classification

### A Simple Example of Scikit-learn Pipeline

> Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya


### 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 = "figs_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))
```