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A supervised learning task that assigns each instance to one of more than two mutually exclusive classes.
DESCRIPTION
Multi-class classification extends binary classification to multiple classes. Strategies include softmax-based models, one-vs-rest, and one-vs-one approaches. Performance is evaluated via macro/micro-averaged precision, recall, and F1. Handling class imbalance and calibrating probabilities improve reliability. Applications include image recognition, text categorization, and intent detection.
ALTERNATIVE DEFINITIONS
Wikipedia
Multiclass classification is a classification task with more than two classes.
Defines the task by the number of classes.
scikit-learn
This module implements multiclass and multioutput algorithms.
Focuses on practical algorithms and strategies for multiclass problems.
RELATED TERMS
Softmax One-vs-Rest One-vs-One Confusion Matrix F1 Score Log Loss Class Imbalance Calibration
COMPETING TERMS
Binary Classification Multi-label Classification
RELATIONS
- Broader: Classification
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Multiclass classification
- scikit-learn - Multiclass strategies in scikit-learn
- TensorFlow - Keras tutorial on classification
TOP QUESTIONS
What is Multi-class Classification?
It is a supervised learning setup assigning each instance to one of several classes beyond binary options.
Category: Definition
How do you implement Multi-class Classification?
Use softmax layers, one-vs-rest or one-vs-one strategies, and appropriate loss functions like cross-entropy.
Category: Requirements
What metrics are used for Multi-class Classification?
Macro/micro-averaged precision, recall, F1, accuracy, and confusion matrices are commonly used.
Category: Testing
What are common mistakes in Multi-class Classification?
Ignoring class imbalance, miscalibrated probabilities, and inadequate validation can distort performance.
Category: Mistakes
What are the risks in Multi-class Classification?
Misclassification costs vary by class; poorly handled imbalance can harm critical predictions.
Category: Risks
How does Multi-class compare to Multi-label Classification?
Multi-class assigns one exclusive class; multi-label allows multiple labels per instance.
Category: Alternatives
What is a hot topic in Multi-class Classification?
Calibrated classifiers and cost-sensitive learning for imbalanced tasks are active areas.
Category: Topic
How should data be prepared for Multi-class Classification?
Ensure representative class distributions, perform stratified splits, and use appropriate preprocessing for features.
Category: Best practices
Where to start learning Multi-class Classification?
Explore scikit-learn’s multiclass module and TensorFlow tutorials focusing on softmax classifiers.
Category: Overview