To address overfitting, HCIP-AI EI Developer V2.5 outlines multiple strategies:
* Dropout:A regularization method that randomly ignores certain neurons during training, preventing reliance on specific paths and improving generalization.
* Data augmentation:Expands the training dataset by applying transformations (rotation, scaling, flipping) to existing data, increasing diversity and reducing overfitting risk.
* Parameter norm penalties:Techniques such as L1 and L2 regularization add a penalty to large parameter values, discouraging overly complex models.
Using amore complex model(Option B) is the opposite of what is recommended, as it generally increases the risk of overfitting.
Exact Extract from HCIP-AI EI Developer V2.5:
"Common overfitting mitigation techniques include data augmentation to expand datasets, dropout to randomly deactivate neurons during training, and applying regularization penalties to constrain model complexity." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Preventing Overfitting