Explore your Data with Qlik AutoML® Connector
In our QQblog article on SHAP data: https://qqinfo.ro/en/the-explain-ability-data-shap-data/, we presented a video where it was briefly explained what SHAP Data was and how it is used to explain th “Why” behind the predictions.
The video at the end of this article, is going to show you how to add the SHAP Data to your preditions Qlik Sense™ App along with some of the ways it can be used to analyze the influence behind the predictions and eventually use this data to explore some new predictive scenarios with Qlik AutoML® connector. This video helps you become more familiar with AutoML in the simpliest way possible.
We will go on with a video detailing 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.
*The Coordinates SHAP (Shapley Additive exPlanations) refer to the values that explain how much each feature (input variable) contributes to a specific prediction made by a machine learning model. These coordinates represent the marginal contribution of each feature in terms of its influence on the prediction.
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