Raw Data to Features and Good vs Bad Features >>> Before being input into an ML model raw data must be turned into >>> Feature Engineering
Before being input into an ML model, raw data must be turned into:
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.
A good feature should have which of the following characteristics?
True or False: Different problems in the same domain may need different features.