Binary-vs-Multiclass Classification:

Binary classification and multiclass classification are two types of machine learning tasks that involve assigning items to different categories or classes based on certain features. The primary difference between them lies in the number of classes or categories that the model needs to predict.

  1. Binary Classification:
    • Definition: In binary classification, the task is to categorize items into one of two classes or categories.
    • Example: Spam detection (spam or not spam), medical diagnosis (disease or no disease), sentiment analysis (positive or negative sentiment).
    • Output: The model outputs a probability or a label indicating the likelihood of the item belonging to one of the two classes.
  2. Multiclass Classification:
    • Definition: In multiclass classification, the task involves assigning items to one of more than two classes or categories.
    • Example: Handwriting recognition (recognizing digits 0-9), object recognition (identifying different types of animals), language identification (determining the language of a text among multiple possibilities).
    • Output: The model outputs a probability distribution across all classes, and the class with the highest probability is considered the final prediction.

 

Key differences:

Feature

Binary Classification

Multiclass Classification

Number of Classes

2

3 or more

Model Output

Single probability or label

Probability distribution

Evaluation Metrics

Accuracy, precision, recall, F1, AUC-ROC

Accuracy, precision, recall, F1, confusion matrix

Model Architecture

Single output node with sigmoid activation

Multiple output nodes with softmax activation

Training Considerations

Simpler to train and interpret

More complex architectures, class imbalance considerations