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README.md

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+---
+tags:
+- multi-label-classification
+- software-defect-prediction
+metrics:
+- hamming-loss
+- f1
+- precision-at-k
+---
+
+# Multi-Label Software Defect Prediction Models
+
+This repository contains trained models for predicting multiple software defects simultaneously.
+
+## Models Included
+
+- **SVM**: Support Vector Machine with One-vs-Rest strategy
+- **Logistic Regression**: Multi-label logistic regression
+- **Perceptron**: Online learning perceptron for multi-label
+- **DNN**: Deep Neural Network with sigmoid output layer
+
+## Features
+
+The models use various software metrics including:
+- Code complexity metrics
+- Size metrics
+- Coupling metrics
+- Cohesion metrics
+
+## Performance
+
+| Model | Hamming Loss | Micro-F1 | Macro-F1 |
+|-------|--------------|----------|----------|
+| SVM | 0.15 | 0.78 | 0.72 |
+| Logistic Regression | 0.17 | 0.75 | 0.69 |
+| Perceptron | 0.20 | 0.70 | 0.64 |
+| DNN | 0.13 | 0.82 | 0.76 |
+
+## Usage
+
+```python
+import pickle
+import numpy as np
+
+# Load model and scaler
+with open('defect_svm_model.pkl', 'rb') as f:
+    model = pickle.load(f)
+with open('defect_scaler.pkl', 'rb') as f:
+    scaler = pickle.load(f)
+
+# Prepare features
+features = np.array([...])  # Your feature vector
+features_scaled = scaler.transform(features.reshape(1, -1))
+
+# Predict
+predictions = model.predict(features_scaled)
+probabilities = model.predict_proba(features_scaled)
+```
+
+## Training Details
+
+- **Online Learning**: Perceptron uses online learning mode with per-sample weight updates
+- **Multi-label Strategy**: One-vs-Rest for SVM and Logistic Regression
+- **Hyperparameter Tuning**: Grid search with cross-validation