Multi-Class Classification

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)

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