# 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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### 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: ```python from huggingface_hub import hf_hub_download import skops.io as sio import pandas as pd # Download model and test data hf_hub_download(repo_id='CSC310-fall25/wine-quality-regression', filename='model.pkl', local_dir='.') hf_hub_download(repo_id='CSC310-fall25/wine-quality-regression', filename='test_data.csv', local_dir='.') # Load model and data model = sio.load('model.pkl') test_data = pd.read_csv('test_data.csv') # Prepare features and target X_test = test_data.drop('quality', axis=1) y_test = test_data['quality'] # Make predictions y_pred = model.predict(X_test) ``` # Model Card Authors Christian Romualdo # Model Card Contact cromualdo@uri.edu # Citation This dataset is from UCI Machine Learning Repository. To learn more, visit: https://archive.ics.uci.edu/dataset/186/wine+quality # Intended 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.

	fit_intercept	True
	copy_X	True
	tol	1e-06
	n_jobs	None
	positive	False