Lição de Aprendizado de Máquina do Dia – Classificação e Regressão

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. […]”