Measuring Model Accuracy
Explore key metrics for evaluating classification model accuracy including accuracy, confusion matrices, sensitivity, and specificity. Understand their roles in assessing model performance beyond simple accuracy to ensure robust and meaningful predictions across binary and multiclass problems.
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Quality of classification model predictions
So far, this course has relied on accuracy to determine the value of CART classification tree models. Using accuracy has been helpful because a large audience easily understands the accuracy measure (i.e., the percentage of correct predictions).
However, despite accuracy being the default metric for classification problems, there are other, or better, metrics to evaluate the quality of predictions. This lesson covers several ways of measuring the quality of classification model predictions:
Accuracy
Confusion matrices
Sensitivity
Specificity
Note: While this lesson uses binary classification examples, the techniques also apply to multiclass scenarios (i.e., three or more unique label values).
Accuracy
The most ...