In this video, we breakdown the analytics you can create from the provided Coordinate SHAP data when used within Qlik AutoML. SHAP data helps determine the WHY behind the machine learning model predictions. Learn how SHAP values break down the influence of individual features on specific outcomes, helping you gain deeper insights into model behavior. This is part 5 of the Qlik AutoML series, focusing on making machine learning more interpretable for users.

This video shows you how to add the SHAP data to your Qlik Sense App - so you can prepare analytics about the influencers behind the predictions and eventually use this data to explore some new predictive scenarios with our Qlik AutoML connector.

This video briefly describes the Explain-ability data also known as SHAP data. Next video will be released shortly!

This video shows you how to use the predictive model data created in video 1 to create a very simple predictive Qlik Sense App.

Predict, Explain, Act with Qlik AutoML, a powerful tool that brings automated machine learning to the hands of business users and data analysts. In this all new series, revist and learn how Qlik AutoML allows you to build predictive models without needing deep technical expertise in data science. Follow along in the next few videos linked in the description as Mike Tarallo walks you through the key features, from experiment to deployment, and see how Explain-ability is defined and used to gain insights that drive decision-making. Perfect for those looking to enhance their analytics with AI-powered predictions!

See how easy it is to create a Qlik AutoML Predictive Model.