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---
license: mit
---
# Dockerfile Commit Classification Model
This is a Logistic Regression model enhanced with a rule-based system for multi-label classification of Dockerfile-related commit messages. It combines machine learning with domain-specific rules to achieve accurate categorization.
## Files
- `logistic_model.joblib`: Trained Logistic Regression model.
- `tfidf_vectorizer.joblib`: TF-IDF vectorizer for text preprocessing.
- `label_binarizer.joblib`: MultiLabelBinarizer for encoding/decoding labels.
## Features
- **Hybrid Approach**: Combines machine learning with rule-based adjustments for better classification.
- **Dockerfile-Specific Labels**: Categorizes commit messages into predefined classes:
- `bug fix`
- `code refactoring`
- `feature addition`
- `maintenance/other`
- `Not enough information`
- **Multi-Label Support**: Each commit message can belong to multiple categories.
## How to Use
To use this model, load the files and preprocess your data as follows:
```python
from joblib import load
# Load the model and preprocessing artifacts
model = load("logistic_model.joblib")
tfidf_vectorizer = load("tfidf_vectorizer.joblib")
mlb = load("label_binarizer.joblib")
# Example usage
new_messages = [
"Fixed an issue with the base image in Dockerfile",
"Added multistage builds to reduce image size",
"Updated Python version in Dockerfile to 3.10"
]
X_new_tfidf = tfidf_vectorizer.transform(new_messages)
# Predict the labels
predictions = model.predict(X_new_tfidf)
predicted_labels = mlb.inverse_transform(predictions)
# Print results
for msg, labels in zip(new_messages, predicted_labels):
print(f"Message: {msg}")
print(f"Predicted Labels: {', '.join(labels) if labels else 'No labels'}\n")
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