# Lição de Aprendizado de Máquina do Dia – Classificação e Regressão

2014 Apr 07Uma explicação que cabe uma reflexão:

*“[…]Thus, regression in statistics is different from regression in supervised learning.* *In statistics,* *• regression is used to model relationships between predictors and targets, and the targets could be continuous or categorical.* *• a regression model usually includes 2 components to describe such relationships:* *o a systematic component* *o a random component. The random component of this relationship is mathematically described by some probability distribution.* *• most regression models in statistics also have assumptions about thestatistical independence or dependence between the predictors and/or between the observations.* *• many statistical models also aim to provide interpretable relationships between the predictors and targets.* *o For example, in simple linear regression, the slope parameter, , predicts the change in the target, , for every unit increase in the predictor, .* *In supervised learning,* *• target variables in regression must be continuous* *• regression has less or even no emphasis on using probability to describe the random variation between the predictor and the target* *o Random forests are powerful tools for both classification and regression, but they do not use probability to describe the relationship between the predictors and the target.* *• regression has less or even no emphasis on providing interpretable relationships between the predictors and targets.* *o Neural networks are powerful tools for both classification and regression, but they do not provide interpretable relationships between the predictors and the target.* *[…]”*