Neural networks: further insights into error function, generalized weights and others

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• This article extends previous work on neural network (NN) structures.
• Introduces fundamental NN concepts: activation function, error function, learning rate, and generalized weights.

Discussion:

• NN topology visualization is achieved using a generic plot() function with an 'nn' class object.
• Generalized weights enable the interpretation of individual input variable effects within NN models.

Key Insights:

• Large variance in generalized weights suggests non-linear effects of a covariate.
• Generalized weights near zero indicate a covariate has no significant impact on the outcome.
• The compute() function facilitates predictions for new observations, requiring input features in the original training order.

Outlook:

• Further exploration of generalized weights for deeper NN interpretability.
• Development of advanced visualization techniques for complex NN architectures.
• Application of these NN interpretation methods to diverse scientific domains.