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SHAP: Bridging the Gap Between Machine Predictions and Actionable Recommendations


In today's data-centric landscape, much fanfare surrounds the predictive prowess of machine learning, often casting it as the beacon of the digital age. Yet, while prediction is undeniably a potent facet, the true crown jewel of machine learning lies in its prescriptive capabilities. Moving beyond simply forecasting future events, the power of prescriptive analysis provides actionable insights and recommendations, enabling organizations to not just foresee but actively shape their desired outcomes. At the heart of this prescriptive potential is a tool that ensures clarity and understanding – the SHAP statistics.


We have published an eBook recently about Machine Learning in Qlik AutoML and Prescriptive Analytics (link here) but we also found a need to get a dep dive into one of the key tools to take the use of Machine Learning in companies to the next level. We than transformed an old blog post of our lead data scientist into this document.


Click on the following link to download the PDF file. Enjoy your reading.


ipc_data_science_shap
.pdf
Download PDF • 334KB


If you are interested in knowing more about how your company can take advantage of this valuable concept, contact our data science team, and let's discuss how science can transform your business. Our experts are ready to help you navigate the complexities of machine learning and data analytics, ensuring that you can fully leverage the potential of Qlik AutoML to achieve your strategic objectives.

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