Describe how the concepts of features, targets, training, and testing are interrelated in Machine Learning.
- Features and targets are for clustering; Training and testing for prediction
- Features and targets are unrelated; Training and testing are used interchangeably
- Features are for prediction; Targets for evaluation; Training and testing are unrelated
- Features are used to predict targets; Training is learning patterns; Testing evaluates performance
Features are the input variables used to predict targets. Training involves learning the patterns from features to predict targets, and testing evaluates how well this learning generalizes to unseen data. These concepts are essential in building and evaluating supervised learning models.
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