Conclusion, IRIS dataset is – due to the nature of the measurments and observations – robust and rigid; one can get very good accuracy results on a small training set. Everything beyond 30% for training the model, is for this particular case, just additional overload.
The general concept here is, how small can you arbitrarily slice the data and still come up with the same result as the overall data set? Or, phrased differently, how much data do you need to collect before predictions stabilize? Read on to see how Tomaz solves the problem.