The data to which a ML algorithm is applied is called a training set, which consists of a set of pairs (`x`

, `y`

), called training examples. The pairs are explained as follows:

`x`

: This is a vector of values, often called the feature vector. Each value, or feature, can be categorical (values are taken from a set of discrete values, such as`{S, M, L}`

) or numerical.`y`

: This is the label, the classification or regression values for`x`

.

The objective of the ML process is to discover a function that best predicts the value of `y`

associated with each value of `x`

. The type of `y`

is in principle arbitrary, but there are several common and important ...

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