The following flowchart can be used to decide the ensemble method that is most appropriate for a given scenario: Start * Is the goal to reduce variance or bias?    * If reducing variance, then use bagging or random forest.    * If reducing bias, then use boosting. * Is the data noisy?    * If yes, then use bagging or random forest.    * If no, then use boosting. * Is the data imbalanced?    * If yes, then use weighted averaging or stacking.    * If no, then use any of the ensemble methods. End   Are there exceptions to the flow?  Does the data play a role?

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Temporal difference learning

Temporal difference learning (TD learning) is a concept in machine learning that attributes to a class of model-free reinforcement learning methods. The technique predicts a reward or quantity depending on the future values of the value function.