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Utilizing Power BI dashboards to explain model predictions entails connecting machine learning model outputs with simple visual representation. In general, the process begins with ingesting the prediction results—such as probabilities, classifications, or regression scores—into Power BI from a data source (Azure ML, a Python script, or an API). Secondly, use visual tools, such as bar charts, scatter plots, and decomposition trees, to show how input features contribute to the outputs. If the audience is receptive, leveraging techniques such as SHAP (SHapley Additive exPlanations) can explain contributions of features and assist the viewer in understanding why a particular prediction was made.


For practitioners wishing to improve the story-telling aspects of dashboards, considering a Power BI Course in Pune will provide instruction on how to integrate model results with clarifying visuals, including developing calculated columns, tool-tips, and conditional formatting to help organize what the viewer should focus on.


On the other hand, by completing advanced Power BI Training in Pune, learners will ultimately repackage and reflect on the AI-driven insights against the business KPIs, weaving perception and business together into more contextual, explainable dashboards overall to create that link. This approach is critical for ensuring transparency between inputs and outputs, which can improve feelings of trust in the model's predictions. This latter connection is even more relevant for highly regulated industries, such as finance, healthcare, and marketing, where justification is important for understanding model reasoning.

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