Multi-Class Classification
Multi-class classification is a type of supervised machine learning where the model learns to assign an input to one of three or more possible categories (classes).
🧠 What It Means
* Unlike binary classification (yes/no, positive/negative), multi-class models can handle problems like:
- Classifying types of brain tumors
- Identifying disease subtypes
- Differentiating among several anatomical lesions
Example:
- Class 0 → Pituitary adenoma
- Class 1 → Craniopharyngioma
- Class 2 → Rathke’s cleft cyst
- Class 3 → Tuberculum sellae meningioma
⚙️ How It Works
1. Input: Feature vectors (e.g., radiomics from MRI) 2. Model: Trained using labeled data with more than two output classes 3. Prediction: Model outputs either:
- A single class label (e.g., 2 = RCC)
- A probability distribution over all classes
🚀 Algorithms That Support Multi-Class
| Algorithm | Strategy Used |
|---|---|
| XGBoost | Native support via `multi:softmax` or `multi:softprob` |
| Logistic Regression | Multinomial extension |
| Support Vector Machine (SVM) | One-vs-rest or one-vs-one |
| Random Forest | Native multi-class |
| Neural Networks | Softmax activation in output layer |
📏 Evaluation Metrics
| Metric | Notes |
|---|---|
| Accuracy | Percentage of correct predictions |
| Confusion Matrix | Shows how predictions match actual labels |
| Precision / Recall / F1-score | Often calculated per class |
| AUC (macro/micro average) | Useful when using probabilistic outputs |
🏥 Medical Use Cases
* Differentiating sellar region lesions using MRI * Classifying skin lesions or lung nodules * Assigning patients to risk categories or cancer subtypes * Diagnosing neurological conditions with overlapping symptoms
💡 Tip
Multi-class problems may require:
- More data per class
- Class balancing techniques (e.g., SMOTE)
- Careful cross-validation (e.g., stratified k-fold)