COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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True
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False
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Either A or B
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None of the above
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Detailed explanation-1: -With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you’re attempting to predict or forecast. Another common example with regression might be to try to predict the dollar value of an insurance policy premium for someone.
Detailed explanation-2: -For supervised learning to work, you need a labeled set of data that the model can learn from to make correct decisions. Data labeling typically starts by asking humans to make judgments about a given piece of unlabeled data.
Detailed explanation-3: -A feature is one column of the data in your input set. For instance, if you’re trying to predict the type of pet someone will choose, your input features might include age, home region, family income, etc. The label is the final choice, such as dog, fish, iguana, rock, etc.
Detailed explanation-4: -A label is the thing we’re predicting-the y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.