classification

a procedure which computes an unknown parameter for a given element

classification model

An algorithm which, given an individual for which the class is not known, computes the class. The algorithm make use of parameters for tuning

the core of a classification model is the decision function.

• DECISION FUNCTION (the model)

  function that makes a prediction on the class element as

  $$ M(x,\theta) = y(x)pred $$

where $\theta$ is a set of values for the parameters of the decision function

vapnik-chervonenkis dimension

if a classification model is able to shatter all possible problems with N elements it’s Vapnik-Chervonenkis dimension is equal to $N$

classification workflow

• use the training dataset for tuning parameters

• use the training set against the classification model obtained at the previous step to calculate the accuracy

classification types

• CRISP

  the classifier assigns to each individual one label

• PROBABILISTIC

  the classifier assigns a probability for each of the possible labels