Hands-On Transfer Learning with Python

Model evaluation

A model is basically a generalized representation of data and the underlying algorithm used for learning this representation. Thus, model evaluation is the process of evaluating the built model against certain criteria to assess its performance. Model performance is usually a function defined to provide a numerical value to help us decide the effectiveness of any model. Often, cost or loss functions are optimized to build an accurate model based on these evaluation metrics.

Depending upon the modeling technique used, we leverage relevant evaluation metrics. For supervised methods, we usually leverage the following techniques:

• Creating a confusion matrix based on model predictions versus actual values. This covers metrics ...