In machine learning and forecasting, a "deep feature" of the is its ability to be decomposed into three specific components that explain why a model is performing a certain way:
: The undersides of the leaves and the flower stalks (pedicels) are densely covered in tiny, sticky glandular hairs . In machine learning and forecasting, a "deep feature"
: Measures how close the predicted probabilities are to the actual true frequencies. If you predict a 70% chance of rain, it should actually rain 70% of the time. The most distinctive "deep" feature of the (
The most distinctive "deep" feature of the ( Rosa rubiginosa ) is its scented foliage , which sets it apart from almost all other wild roses. 0.5) has no resolution.
: Measures how much the predictions differ from the overall base rate. A model that always predicts the average (e.g., 0.5) has no resolution.