A person is given a machine learning problem that has real-valued attributes in a 100-dimensional space.They estimates that only 10 attributes are relevant for this very large dataset. Which machine learning algorithm would you advice the person  to use given a choice of decision tree and k-nearest neighbour? Explain the choice and how they should approach this task?

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Q: probabilistic

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Q: How does Random Forest work? Why is it better than a single decision tree?

A: Since you have asked multiple questions, we will solve the first question for you. If you want any…

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A: I Have answered this question in step 2.

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A: Below  is  the  solution  :

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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.