What is gradient boosting of decision trees in SAP SCM-IBP-DM - Demand?

SAP Term: gradient boosting of decision trees

• Component: SCM-IBP-DM

• Component Name: Demand

• Description: A machine learning algorithm used to calculate optimized forecasts in demand planning. The algorithm calculates a forecast after gradually optimizing the way in which the selected independent variables should be applied during the regression process. For this purpose, the algorithm creates a decision tree that defines in what order and along which thresholds the independent variables should be considered. The input of this decision tree is a historical value, while the output is an ex-post forecast. These two are expected to be the same. If they are different, the algorithm attempts to reduce the error by creating an additional tree. The input of this new tree is the error from the previous tree, and the order of variables, the thresholds, or both are different from those previously used. For example, in the first decision tree the temperature is considered first, while in the next tree it is only considered after two other variables. Further trees are created until the error is minimized

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• Key Concepts: 
  Gradient boosting of decision trees is a machine learning technique used to improve the accuracy of predictive models. It combines multiple decision trees into an ensemble model, which is then used to make predictions. The technique works by sequentially adding decision trees to the model, each tree being trained on the residuals of the previous tree. This allows the model to learn from its mistakes and improve its accuracy over time. 
  
  How to use it: 
  In SAP's SCM-IBP-DM Demand Forecasting component, gradient boosting of decision trees can be used to create more accurate forecasts. To do this, the user must first define the parameters for the model, such as the number of trees and the learning rate. Once these parameters are set, the model can be trained on historical data and used to make predictions. 
  
  Tips & Tricks: 
  When using gradient boosting of decision trees in SAP's SCM-IBP-DM Demand Forecasting component, it is important to tune the parameters of the model for optimal performance. This can be done by testing different combinations of parameters and evaluating their performance on a validation dataset. Additionally, it is important to ensure that the data used for training is up-to-date and relevant to the task at hand. 
  
  Related Information: 
  Gradient boosting of decision trees is a powerful machine learning technique that can be used in many different applications. It is also related to other machine learning techniques such as random forests and support vector machines. For more information about these techniques and how they can be used in SAP's SCM-IBP-DM Demand Forecasting component, please refer to SAP's documentation or contact an SAP expert.