Which is more important in designing the machine learning model: model performance or model accuracy? Justify your answer?
Answers
Answer:
Model Accuracy
Explanation:
I am assuming that by model performance you mean its speed. Note that model performance, in scientific terms, could refer to its quality as well, and in this case accuracy would be one way to measure performance.
If you meant speed, this is of course very dependent on the application. For example, in analysis of medical images to determine if there is a disease (such as cancer), the accuracy extremely critical, even if the models would take minutes or hours to make a prediction.
Other applications require real time performance, even if this comes at a cost of accuracy. For example, imagine a machine that views a fast conveyor belt carrying tomatoes, where it must separate the green from the red ones. Though an occasional error is undesired, the success of this machine is more determined by its ability to withstand its throughput.
A more common example is face detection for recreational applications. People would expect a fast response from the app, though the occasional missed face would not render it useless.