APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Supervised learning
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Unsupervised learning
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Reinforcement learning
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All
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Detailed explanation-1: -The aim of supervised, machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. As adaptive algorithms identify patterns in data, a computer “learns” from the observations. When exposed to more observations, the computer improves its predictive performance.
Detailed explanation-2: -Linear Regression Linear regression is perhaps one of the most well-known and well-understood algorithms in statistics and machine learning. Predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability.
Detailed explanation-3: -Semi-supervised Learning So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building.