[amp_mcq option1=”Active learning” option2=”Reinforcement learning” option3=”Supervised learning” option4=”Unsupervised learning” correct=”option2″]<\/p>\n
The answer is B. Reinforcement learning.<\/p>\n
In reinforcement learning, an agent learns to take actions in an environment in order to maximize a reward signal. The agent is not explicitly told what to do, but instead learns through trial and error. The agent is given a reward signal that indicates how well it is doing, and it learns to take actions that maximize the reward.<\/p>\n
In supervised learning, an agent is given a set of training data, which consists of inputs and outputs. The agent learns to map the inputs to the outputs. The agent is not given any feedback on how well it is doing, but instead learns to minimize the error between its predictions and the actual outputs.<\/p>\n
In unsupervised learning, an agent is given a set of data, but no labels. The agent learns to find patterns in the data. The agent is not given any feedback on how well it is doing, but instead learns to find structure in the data.<\/p>\n
Active learning is a type of supervised learning in which the learner is allowed to choose the data that it is trained on. This can be useful in cases where the data is expensive to collect or where the learner has prior knowledge about the data.<\/p>\n
In conclusion, the correct answer is B. Reinforcement learning.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Active learning” option2=”Reinforcement learning” option3=”Supervised learning” option4=”Unsupervised learning” correct=”option2″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[676],"tags":[],"class_list":["post-17592","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","no-featured-image-padding"],"yoast_head":"\n