Uma 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.*

*[…]”*