getNumClasses

Returns the default number of output classes for classification tasks.

This method provides the standard output dimensionality used for multi-class classification when no explicit class count is specified.

Default Configuration:

• Output Classes: 4 categories

• Activation: Softmax for probability distribution over classes

• Loss Function: Categorical crossentropy (planned implementation)

Classification Examples:

Class 0: "Category A" → Output[0] = probability
Class 1: "Category B" → Output[1] = probability
Class 2: "Category C" → Output[2] = probability
Class 3: "Category D" → Output[3] = probability
Total: Σ(probabilities) = 1.0

Common 4-Class Applications:

• Sentiment Analysis: Positive, Negative, Neutral, Mixed

• Direction Classification: North, South, East, West

• Quality Assessment: Excellent, Good, Fair, Poor

• Priority Levels: Critical, High, Medium, Low

Mathematical Properties:

• Output Range: 0, 1 per class via softmax activation

• Constraint: Σ(class_probabilities) = 1.0

• Decision Rule: argmax(output_vector) for classification

Scalability Notes:

• Easily configurable via createModel numClasses parameter

• Memory scales linearly with class count

• Training complexity increases with class imbalance

See also