Is it recommended to compute the average of metrics for classification? For computing the performance metrics of the models used for classification. Is it recommended to repeat the experiments many times (for example 10 times) and compute the average? In my case, I used 116745 samples for training and 125927 for testing.
Yes, you can try k-fold validation, where indicidual models are generated from various subsets of the data and performance measured on a validation set.
You can the compute the average and variance of model accuracy, other performance metrics to understand how stable your model is.