What term refers to the variables used to explain the target in a predictive model?

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Multiple Choice

What term refers to the variables used to explain the target in a predictive model?

In predictive modeling, the inputs the model uses to explain or predict the outcome are called features. They are the observed attributes or measurements that the model learns from to establish the relationship with the target variable. You can think of them as the predictors or explanatory variables, though the common term in practice is features. The other terms refer to things you examine after modeling—like heteroskedasticity, which describes changing variance in the errors; Cook's Distance, which identifies influential observations; and residual plots, which diagnose the residuals—so they aren’t the input variables that drive the predictions.

In predictive modeling, the inputs the model uses to explain or predict the outcome are called features. They are the observed attributes or measurements that the model learns from to establish the relationship with the target variable. You can think of them as the predictors or explanatory variables, though the common term in practice is features. The other terms refer to things you examine after modeling—like heteroskedasticity, which describes changing variance in the errors; Cook's Distance, which identifies influential observations; and residual plots, which diagnose the residuals—so they aren’t the input variables that drive the predictions.

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