How many times should training data be resampled to reduce variance? Is there a general rule for the number of times training data for a model should be resampled to reduce variance?
Here is an example
Run 1:
Sample 1) 0.8431
Sample 2) 0.8430
Sample 3) 0.8431
Sample 4) 0.8432
Standard deviation of samples 0.0000816496580927636
Run 2
Sample 1) 0.55647
Sample 2) 0.4538
Sample 3) 0.65217
Sample 4) 0.6
Standard deviation of samples 0.0841829780893967
In run 1 the results were pretty much the same every time, so I don't see a reason to sample more than once. In run 2 the standard deviation is much higher. Run 2 would require resampling to reduce the model variance and give accurate predictions. How do I decide on the number of times to resample run 2?

This is actually model variance and the results may not change much between iterations. So, if there is inherent variance to the model, it's impossible to decrease it by performing many resamples. You'll only estimate this variance better by increasing the number of resamples.
For example, if your model was outputting a constant, e.g. 5, the variance would be $0$, and increasing the number of resamples wouldn't change this.
Similarly, if your model is totally outputting random numbers with some variance, $\sigma^2$, it's still not possible to decrease it by increasing the number of resamples.
