Model description
This is a Linear Regression model trained on combined red and white wine quality data from UCI Machine Learning Repo. The goal of this model is to predict wine quality scores (3-9) based on 12 physicochemical features including wine type.
Intended uses & limitations
[More Information Needed]
Training Procedure
[More Information Needed]
Hyperparameters
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| Hyperparameter | Value |
|---|
| copy_X | True |
| fit_intercept | True |
| n_jobs | None |
| positive | False |
| tol | 1e-06 |
Model Plot
LinearRegression()
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Evaluation Results
[More Information Needed]
How to Get Started with the Model
Start by making a notebook for your eval, then use this starter code:
1from huggingface_hub import hf_hub_download
2import skops.io as sio
3import pandas as pd
4
5# Download model and test data
6hf_hub_download(repo_id='CSC310-fall25/wine-quality-regression', filename='model.pkl', local_dir='.')
7hf_hub_download(repo_id='CSC310-fall25/wine-quality-regression', filename='test_data.csv', local_dir='.')
8
9# Load model and data
10model = sio.load('model.pkl')
11test_data = pd.read_csv('test_data.csv')
12
13# Prepare features and target
14X_test = test_data.drop('quality', axis=1)
15y_test = test_data['quality']
16
17# Make predictions
18y_pred = model.predict(X_test)
Model Card Authors
Christian Romualdo
Model Card Contact
Citation
This dataset is from UCI Machine Learning Repository. To learn more, visit:
https://archive.ics.uci.edu/dataset/186/wine+qualityIntended uses & limitations
This model is made for educational purposes and is not ready to be used in production.
Training Procedure
I used the scikit-learn linear regression model on a dataset of 5,320 wine samples. The data was split into 80% training and 20% testing, with the training set further split into 75%/25% for validation. The target value is quality and there are 12 features (11 numeric + 1 categorical for wine type). Evaluation metrics used are MAE and R² score.
Evaluation Results
The model achieved an R² score of 0.284 and MAE of 0.580 points on the validation set. The R² score indicates that the model explains about 28.4% of the variance in wine quality. While the model captures some relationships between physicochemical properties and quality, the moderate performance suggests that linear regression may be too simple for this complex task. The residual plots show patterns indicating that a more complex model might better capture the underlying relationships.
