Titlebook: Demand Prediction in Retail; A Practical Guide to Maxime C. Cohen,Paul-Emile Gras,Renyu Zhang Textbook 2022 The Editor(s) (if applicable) a

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Evaluation and Visualization,In this chapter, we summarize all the results obtained so far and compare the different methods in terms of prediction accuracy. We then present several simple ways to visualize and communicate the prediction results. Finally, we consider varying the ratio of the train-test data split to assess the robustness of our results.

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Demand Prediction in Retail978-3-030-85855-1Series ISSN 2365-6395 Series E-ISSN 2365-6409

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https://doi.org/10.1007/978-3-531-92479-3ypes of methods: Decision Tree, Random Forest, and Gradient Boosted Tree. We apply these methods under both the centralized and decentralized approaches. For each method, we briefly discuss the underlying mathematical framework, present a common practical way to select the parameters, and detail the

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Die Privatisierung von Krankenh?usern aggregate the data across different SKUs to improve the prediction accuracy. On the one hand, aggregating sales data across several SKUs will help reduce the noise and would allow the model to rely on a larger number of observations. On the other hand, this will overlook the fact that each SKU bear

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