AUC (in Binary Classification Only) is used to evaluate how well a binary classification model is able to distinguish between true positives and false positives. An AUC of 1 indicates a perfect classifier, while an AUC of .5 indicates a poor classifier, whose performance is no better than random guessing.
Coefficients indicate the relationship of independent variables to the dependent variable in a model. Positive coefficients show that as the independent variable moves upwards, so does the dependent variable. Negative coefficients indicate that as the coefficient goes down, so does the dependent variable.
Variable importance is a metric that indicates how much an independent variable contributes to predictions in a model. The higher the value shown for a variable in its ranking, the more important it is to the model generated.
Understanding the significance of predictors provides insights for interpreting results, and also may be useful for improving model quality. For instance, editing data sets to rationalize incorrect or incomplete columns — or removing irrelevant ones — can make models faster and more accurate.
Regression in statistical or machine learning models refers to description of the relationship between a dependent variable (outcome variable) and independent variables (features) in data sets when the values are scalar (continuously variable) real numbers, as opposed to discrete values (integers, enumerations, strings, text vectors, etc.).
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