Raw Data to Features and Good vs Bad Features >>> Before being input into an ML model raw data must be turned into >>> Feature Engineering
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Question 1
Before being input into an ML model, raw data must be turned into:
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Question 3
True or False: Training data sets require several example predictor variables to classify or predict a response. In machine learning, the predictor variables are called features and the responses are called labels.
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Question 2
A good feature should have which of the following characteristics?
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Question 4
True or False: Different problems in the same domain may need different features.
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