  After the revelation that neural networks tend to produce overconfident
predictions, the problem of calibration, which aims to align confidence with
accuracy to enhance the reliability of predictions, has gained significant
importance. Several solutions based on calibration maps have been proposed to
address the problem of recalibrating a trained classifier using additional
datasets. In this paper, we offer an algorithm that transforms the weights of
the last layer of the classifier, distinct from the calibration-map-based
approach. We concentrate on the geometry of the final linear layer,
specifically its angular aspect, and adjust the weights of the corresponding
layer. We name the method Tilt and Average(\textsc{Tna}), and validate the
calibration effect empirically and theoretically. Through this, we demonstrate
that our approach, in addition to the existing calibration-map-based
techniques, can yield improved calibration performance. Code available :
https://github.com/GYYYYYUUUUU/TNA_Angular_Scaling.
