In the absence of observations of future events, observation of the past performance of your model is advisable (and rare). If your confidence in the current accuracy of your model is much higher than the past performance of your models… you may be optimizing for something other than accuracy.
Your criticism is accepted.
For my curiosity: which groups have you observed embracing the practice of introducing data on past model performance when presenting a new model? I failed to provide a source, but is it your impression that this isn’t an area in which most people perform poorly
In the absence of observations of future events, observation of the past performance of your model is advisable (and rare). If your confidence in the current accuracy of your model is much higher than the past performance of your models… you may be optimizing for something other than accuracy.
Agreed, and in particular on data you did not consider in formulating it.
Citation needed.
Your criticism is accepted. For my curiosity: which groups have you observed embracing the practice of introducing data on past model performance when presenting a new model? I failed to provide a source, but is it your impression that this isn’t an area in which most people perform poorly
I don’t know that I’ve observed anyone making explicit practice of it out of a formal setting.