Definition of supervised learning
Supervised learning is a set of techniques that allows future predictions based on behaviors or characteristics analyzed in historical targeted data.
A label is nothing more than the output that the dataset has displayed for historical data, already known.
The prediction obtained is represented by a function where the inputs represent the analyzed characteristics and the output represents the variable to be predicted.
This output function is numeric in regression and categorical type problems in classification problems.
To understand the workings of supervised learning we will go to an example where we have a set of data labeled a priori: 1 apples, 2 citrus fruits, 3 watermelons and 4 bananas. This dataset is bizarrely featured in vectors and with this we train the machine learning algorithm. The algorithm generates a model that we can provide you with new data sets that will classify the new elements you have never seen with what you have learned in the training phase.
Regression as a supervised apprenticeship method aims to predict continuous values from historical data targeted
An example of this method is a customer’s expense estimate based on their income and number of children.
Classification as a supervised apprenticeship method is intended to classify into groups based on historical data targeted.
An example of this method is estimating classify fruits based on color shape, texture, …