Key terminology in Machine Learning

Features/ attributes/ features: features or attributes are the individual measurements that, when combined with other features, make up a training example. 

Instance: made up of features

Training set: the set of training examples we’ll use to train our machine learning algorithms.

Target variable: what we’ll be trying to predict with our machine learning algorithms.

Knowledge representation, what the machine has learned. May be in the form of a set of rules; it may be a probability distribution or an example from the training set. 

 

 

 

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