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seeds-clustering
CSC310-fall25/seeds-clustering
seeds-clustering is a machine learning model from CSC310-fall25. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
K-Means clustering model trained on wheat seeds dataset to identify 3 types of wheat seeds based on 7 morphological features. The model groups seeds into clusters that correspond to different wheat varieties.
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At a glance
- Access
- Public
- Created
- Nov 7, 2025
- Updated
- Nov 7, 2025
- SHA
- 046b3d37
Model card
From the Hugging Face model README
Model description
K-Means clustering model trained on wheat seeds dataset to identify 3 types of wheat seeds based on 7 morphological features. The model groups seeds into clusters that correspond to different wheat varieties.
Intended uses & limitations
[More Information Needed]
Training Procedure
[More Information Needed]
Hyperparameters
<details> <summary> Click to expand </summary>| Hyperparameter | Value |
|---|---|
| algorithm | lloyd |
| copy_x | True |
| init | k-means++ |
| max_iter | 300 |
| n_clusters | 3 |
| n_init | auto |
| random_state | 80 |
| tol | 0.0001 |
| verbose | 0 |
Model Plot
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} </style><body><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>KMeans(n_clusters=3, random_state=80)</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-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>KMeans</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.cluster.KMeans.html">?<span>Documentation for KMeans</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="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('n_clusters',this.parentElement.nextElementSibling)"></i></td><td class="param">n_clusters </td><td class="value">3</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('init',this.parentElement.nextElementSibling)"></i></td><td class="param">init </td><td class="value">'k-means++'</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('n_init',this.parentElement.nextElementSibling)"></i></td><td class="param">n_init </td><td class="value">'auto'</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('max_iter',this.parentElement.nextElementSibling)"></i></td><td class="param">max_iter </td><td class="value">300</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('tol',this.parentElement.nextElementSibling)"></i></td><td class="param">tol </td><td class="value">0.0001</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('verbose',this.parentElement.nextElementSibling)"></i></td><td class="param">verbose </td><td class="value">0</td></tr><tr class="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('random_state',this.parentElement.nextElementSibling)"></i></td><td class="param">random_state </td><td class="value">80</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 </td><td class="value">True</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('algorithm',this.parentElement.nextElementSibling)"></i></td><td class="param">algorithm </td><td class="value">'lloyd'</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);
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Evaluation Results
[More Information Needed]
How to Get Started with the Model
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/seeds-clustering', filename='model.pkl', local_dir='.')
hf_hub_download(repo_id='CSC310-fall25/seeds-clustering', 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 (exclude wheat_type for clustering)
X_test = test_data.drop('wheat_type', axis=1)
# Make cluster predictions
cluster_labels = model.predict(X_test)
Model Card Authors
Christian Romualdo
Model Card Contact
Citation
Wheat Seeds Dataset from UCI Machine Learning Repository
Intended uses & limitations
This clustering model is made for educational purposes and identifies 3 wheat seed types. Performance depends on feature scaling and random initialization.
Training Procedure
Trained K-Means with 3 clusters on wheat seeds morphological data. Used 80% of data for training, 20% for testing. Evaluation metrics: Silhouette Score and Adjusted Mutual Information (AMI).
Evaluation Results
Training Silhouette Score: 0.480 Adjusted Mutual Information: 0.722 The model shows good separation between wheat types, with clusters aligning well with true labels as shown in the confusion matrix and scatter plots.
- Created
- Nov 7, 2025
- Updated
- Nov 7, 2025