Understanding ML model interpretability

Model interpretability refers to the degree of ease with which a human can comprehend the logic behind the ML model’s predictions. Essentially, it is the capability to comprehend how a model arrives at its decisions and which variables are contributing to its forecasts.

Let’s see an example of model interpretability using a deep learning convolutional neural network (CNN) for image classification. I built my model in Python using Keras. For this purpose, I will download the CIFAR-10 dataset directly from keras.datasets: it consists of 60,000 32x32 color images (so 3-channels images) in 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck), with 6,000 images per class. Here, I will share just the body of the model; you can find all the related code in the book’s GitHub repository for data preparation and pre-processing at https://github.com/PacktPublishing/Modern-Generative-AI-with-ChatGPT...