Answer / Pankaj Kumar Dagar

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal is to maximize a cumulative reward signal, which is feedback given by the environment for each action taken by the agent. Reinforcement learning algorithms learn through trial and error, adjusting their policy (strategy) based on the rewards received. Common reinforcement learning techniques include Q-learning, SARSA, and actor-critic methods.

What is regularization? Can you give some examples of regularization techniques?

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Assume that you are working on a data set, explain how would you select important variables?

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Why do we convert categorical variables into factor? Which function is used in r to perform the same?

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What do you mean by finite automata?

1 Answers   Verifone,

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Why do we need to convert categorical variables into factor?

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Define kernel svm?

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What are the smaller dataset techniques?

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What according to you, is the standard approach to supervised learning?

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