getModelArchitectureName

Provides a human-readable description of the underlying model architecture.

This method returns descriptive information about the machine learning model architecture that powers the learning engine. The information is intended for display in user interfaces, debugging tools, and system monitoring dashboards to provide transparency about the AI capabilities and computational complexity.

Architecture description formats:

• Neural networks: "MLP(input:64, hidden:128,64,32, output:16)"

• Ensemble methods: "RandomForest(trees:100, depth:10) + SVM(kernel:rbf)"

• Hybrid models: "CNN(conv:3x3, pool:2x2) + LSTM(units:64) + Dense(32)"

• Custom implementations: "CustomAdapter(features:temporal, algorithm:incremental)"

Information purposes:

• User transparency: Help users understand AI capabilities and limitations

• Performance tuning: Enable administrators to optimize resource allocation

• Debugging support: Assist developers in troubleshooting model behavior

• Compliance reporting: Document AI system characteristics for regulatory requirements