What's the best way to choose a model architecture that's easy to interpret?

When choosing a machine learning model, you must weigh the trade-off between simplicity and complexity. Simpler models are easier to interpret but may not capture intricate data patterns. Complex models like deep neural networks can capture more features but may overfit or lack interpretability. To strike a balance, experiment with different model architectures, compare performance and interpretability metrics, and utilize regularization, pruning, or dropout techniques to manage complexity.

First, one must weigh the trade-off between complexity and simplicity. Simpler models are typically easier to understand, but they could miss certain subtleties and trends in the data. While deep neural networks and other complex models can learn more features and interactions, they also run the risk of overfitting or turning into black boxes. You can experiment with different model architectures, evaluate their interpretability and performance metrics, and minimize complexity using regularization, pruning, or dropout approaches in order to strike a compromise between these factors.

When choosing a machine learning model, consider the trade-off between transparency and opacity. Transparency allows you to understand how the model works, its inputs, outputs, and decision-making process. Opacity means the model's inner workings are hidden or abstracted. Transparent models, like linear regression or decision trees, are easy to interpret but may struggle with complex data. Opaque models, like neural networks or ensemble methods, handle complexity but don't reveal their logic.

A quick way to get started with an ML MVP is to use AutoML. When you want to get a prediction mechanism for your data, but don't want to bother with the inner workings, you can feed it to an AutoML engine, and you'll have a prediction in a few hours. This approach is highly general, and while it might provide good results, most of the times it won't be transparent, and might not beat a specific approach. On the other side, if explainability and control are important, implementing custom models is more time consuming, might incur higher costs, however the quality is likely to be superior, while you have full visibility in the Machine Learning pipeline. There is always a tradeoff between implementation speed, interpretability, and quality.

Interpreting CNNs, given their complexity and hierarchical feature extraction, requires specialized techniques. One approach involves visualizing the activation maps of different layers to understand which parts of the input data trigger specific filters. Grad-CAM is a technique that utilizes the gradients of the target class with respect to the final convolutional layer to highlight important regions in an image. Another method is layer-wise relevance propagation, which decomposes the model's prediction by attributing relevance scores to contribution of individual neurons to the output. Integrated Gradients is a technique that assigns importance values to input features by integrating gradients along the path from a baseline to the input.