README.md

# 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

<details>
<summary> Click to expand </summary>

| Hyperparameter | Value |
| :------------: | :---: |
|     copy_X     |  True |

| fit_intercept  |  True |
|     n_jobs     |  None |

|    positive    | False |

|      tol       | 1e-06 |



</details>



### Model Plot



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}#sk-container-id-4 {color: var(--sklearn-color-text);

}#sk-container-id-4 pre {padding: 0;

}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;

}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);

}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;

}#sk-container-id-4 div.sk-text-repr-fallback {display: none;

}div.sk-parallel-item,

div.sk-serial,

div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;

}/* Parallel-specific style estimator block */#sk-container-id-4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;

}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;

}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;

}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;

}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;

}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;

}/* Serial-specific style estimator block */#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;

}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is

clickable and can be expanded/collapsed.

- Pipeline and ColumnTransformer use this feature and define the default style

- Estimators will overwrite some part of the style using the `sk-estimator` class

*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-4 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);

}/* Toggleable label */

#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: flex;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;align-items: start;justify-content: space-between;gap: 0.5em;

}#sk-container-id-4 label.sk-toggleable__label .caption {font-size: 0.6rem;font-weight: lighter;color: var(--sklearn-color-text-muted);

}#sk-container-id-4 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);

}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);

}/* Toggleable content - dropdown */#sk-container-id-4 div.sk-toggleable__content {display: none;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);

}#sk-container-id-4 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);

}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);

}#sk-container-id-4 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);

}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */display: block;width: 100%;overflow: visible;

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}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);

}#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);

}/* Estimator-specific style *//* Colorize estimator box */

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}#sk-container-id-4 div.sk-label label.sk-toggleable__label,

#sk-container-id-4 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);

}/* On hover, darken the color of the background */

#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);

}/* Label box, darken color on hover, fitted */

#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);

}/* Estimator label */#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;

}#sk-container-id-4 div.sk-label-container {text-align: center;

}/* Estimator-specific */

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}#sk-container-id-4 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);

}/* on hover */

#sk-container-id-4 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);

}#sk-container-id-4 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);

}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,

a:link.sk-estimator-doc-link,

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}.sk-estimator-doc-link.fitted,

a:link.sk-estimator-doc-link.fitted,

a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);

}/* On hover */

div.sk-estimator:hover .sk-estimator-doc-link:hover,

.sk-estimator-doc-link:hover,

div.sk-label-container:hover .sk-estimator-doc-link:hover,

.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;

}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,

.sk-estimator-doc-link.fitted:hover,

div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,

.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;

}/* Span, style for the box shown on hovering the info icon */

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}#sk-container-id-4 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);

}/* On hover */

#sk-container-id-4 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;

}#sk-container-id-4 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);

}.estimator-table summary {padding: .5rem;font-family: monospace;cursor: pointer;

}.estimator-table details[open] {padding-left: 0.1rem;padding-right: 0.1rem;padding-bottom: 0.3rem;

}.estimator-table .parameters-table {margin-left: auto !important;margin-right: auto !important;

}.estimator-table .parameters-table tr:nth-child(odd) {background-color: #fff;

}.estimator-table .parameters-table tr:nth-child(even) {background-color: #f6f6f6;

}.estimator-table .parameters-table tr:hover {background-color: #e0e0e0;

}.estimator-table table td {border: 1px solid rgba(106, 105, 104, 0.232);

}.user-set td {color:rgb(255, 94, 0);text-align: left;

}.user-set td.value pre {color:rgb(255, 94, 0) !important;background-color: transparent !important;

}.default td {color: black;text-align: left;

}.user-set td i,

.default td i {color: black;

}.copy-paste-icon {background-image: url(data:image/svg+xml;base64,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);background-repeat: no-repeat;background-size: 14px 14px;background-position: 0;display: inline-block;width: 14px;height: 14px;cursor: pointer;

}

</style><body><div id="sk-container-id-4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LinearRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix=""><div class="estimator-table"><details><summary>Parameters</summary><table class="parameters-table"><tbody><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('fit_intercept',this.parentElement.nextElementSibling)"></i></td><td class="param">fit_intercept&nbsp;</td><td class="value">True</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('copy_X',this.parentElement.nextElementSibling)"></i></td><td class="param">copy_X&nbsp;</td><td class="value">True</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('tol',this.parentElement.nextElementSibling)"></i></td><td class="param">tol&nbsp;</td><td class="value">1e-06</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('n_jobs',this.parentElement.nextElementSibling)"></i></td><td class="param">n_jobs&nbsp;</td><td class="value">None</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('positive',this.parentElement.nextElementSibling)"></i></td><td class="param">positive&nbsp;</td><td class="value">False</td></tr></tbody></table></details></div></div></div></div></div></div><script>function copyToClipboard(text, element) {// Get the parameter prefix from the closest toggleable contentconst toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;const originalStyle = element.style;const computedStyle = window.getComputedStyle(element);const originalWidth = computedStyle.width;const originalHTML = element.innerHTML.replace('Copied!', '');navigator.clipboard.writeText(fullParamName).then(() => {element.style.width = originalWidth;element.style.color = 'green';element.innerHTML = "Copied!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);}).catch(err => {console.error('Failed to copy:', err);element.style.color = 'red';element.innerHTML = "Failed!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);});return false;

}document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {const toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const paramName = element.parentElement.nextElementSibling.textContent.trim();const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;element.setAttribute('title', fullParamName);

});

</script></body>



## 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.