How can you ensure your machine learning model is easily explainable?

1 Choose a simple model

One way to make your machine learning model more explainable is to choose a simple model that has fewer parameters, features, and layers. A simple model can be easier to comprehend, visualize, and communicate than a complex model that has many hidden components and interactions. For example, a linear regression model can be more explainable than a deep neural network, because you can see how each input variable affects the output variable with a coefficient. A simple model can also be more robust and generalizable, as it can avoid overfitting and noise.

3 Apply explainable methods and tools

A third way to make your machine learning model more explainable is to apply explainable methods and tools that can provide insights into how the model works and why it makes certain predictions. Explainable methods and tools can be divided into two categories: global and local. Global methods and tools aim to explain the overall behavior and logic of the model, such as its structure, parameters, and feature importance. Local methods and tools aim to explain the specific output and decision of the model for a given input, such as its confidence, uncertainty, and counterfactuals. Some examples of explainable methods and tools are SHAP, LIME, and What-If Tool.

4 Evaluate and validate your model

A fourth way to make your machine learning model more explainable is to evaluate and validate your model using various metrics and methods that can measure its performance, quality, and fairness. Evaluating and validating your model can help you identify and correct any errors, biases, or limitations that may affect its explainability and reliability. For example, you can use accuracy, precision, recall, and F1-score to measure the model's ability to make correct predictions, ROC curve and AUC to measure the model's ability to distinguish between classes, and confusion matrix and error analysis to measure the model's ability to handle different cases.

5 Communicate and document your model

A fifth way to make your machine learning model more explainable is to communicate and document your model using clear and concise language, visuals, and examples that can convey the key information and insights of your model to your target audience. Communicating and documenting your model can help you build trust and confidence in your model, as well as facilitate feedback and collaboration. For example, you can use graphs, tables, and charts to show the model's inputs, outputs, and performance, use scenarios, stories, and analogies to illustrate the model's logic and reasoning, and use labels, annotations, and comments to explain the model's code and steps.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?