Does the quality of calibration of a model improve with the training data size or even the number of classes?
Reliability scores in classification models are really important, and calibration variants such as temperature scaling are one step to improve them, although not perfectly.
Would the calibration of the model improve after using temp-scaling if more training data is introduced? This would seem to be the case if this data would improve the accuracy. But would this still be the case if the accuracy is untouched?
And would the calibration of the model improve after using temp-scaling if more classes are in the network? Of course, each class having the same number of training samples.
classification softmax calibration reliability
asked Nov 22 '18 at 15:55
user3337758user3337758
327
add a comment |
Reliability scores in classification models are really important, and calibration variants such as temperature scaling are one step to improve them, although not perfectly.
Would the calibration of the model improve after using temp-scaling if more training data is introduced? This would seem to be the case if this data would improve the accuracy. But would this still be the case if the accuracy is untouched?
And would the calibration of the model improve after using temp-scaling if more classes are in the network? Of course, each class having the same number of training samples.
classification softmax calibration reliability
asked Nov 22 '18 at 15:55
user3337758user3337758
327
add a comment |
Reliability scores in classification models are really important, and calibration variants such as temperature scaling are one step to improve them, although not perfectly.
Would the calibration of the model improve after using temp-scaling if more training data is introduced? This would seem to be the case if this data would improve the accuracy. But would this still be the case if the accuracy is untouched?
And would the calibration of the model improve after using temp-scaling if more classes are in the network? Of course, each class having the same number of training samples.
classification softmax calibration reliability
asked Nov 22 '18 at 15:55
user3337758user3337758
327
Reliability scores in classification models are really important, and calibration variants such as temperature scaling are one step to improve them, although not perfectly.
Would the calibration of the model improve after using temp-scaling if more training data is introduced? This would seem to be the case if this data would improve the accuracy. But would this still be the case if the accuracy is untouched?
And would the calibration of the model improve after using temp-scaling if more classes are in the network? Of course, each class having the same number of training samples.
classification softmax calibration reliability
classification softmax calibration reliability
asked Nov 22 '18 at 15:55
user3337758user3337758
327
asked Nov 22 '18 at 15:55
user3337758user3337758
327
asked Nov 22 '18 at 15:55
user3337758user3337758
327
asked Nov 22 '18 at 15:55
user3337758user3337758
327
asked Nov 22 '18 at 15:55
user3337758user3337758
327
327
add a comment |
add a comment |
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