We are thrilled to announce the latest enhancement to our AutoML product – the integration of SHAP (SHapley Additive exPlanations) values.
Now, you can access the explainability behind the predictions of our models with just one click. While deploying a model built with our Plat AutoML, you can also activate a service to get the SHAP values behind every prediction on a real-time basis.
SHAP values allow users to see the contribution of each feature to the model’s predictions. This level of transparency is crucial for industries where understanding the ‘why’ behind a model’s output is just as important as the accuracy of the predictions.
With this new functionality, users can now obtain SHAP values for each request, demystifying machine learning models’ “black box” nature. This ensures that our clients can confidently deploy our AutoML solutions, knowing they can explain and justify the model’s decisions.
This enhancement will empower our users to build more trustworthy models, fostering greater confidence and compliance in their machine-learning applications.
