Classification Learner App Iterations
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I am performing supervised learning on a binary data set of 20 samples. I am doing holdout variation, training on 80% and testing on 20% of the data. As 20% of the test data is only 4 samples, the accuracy I get out each time I run a classifier varies wildly. I have found a way to manually add repeat iterations by generating the code from the app and adding it in (I am currently using 1000 iterations). However, I would like to be able to use the optimisable classifiers found in the app, but they would only be valuable if I can find a way to add repeat iterations using different holdout samples for each run. Any help would be much appreciated!
Prince Kumar on 7 Apr 2022
Your training sample is too small for the model to learn anything significant. If you train your model for large interations, then your model will overfit.
Hope this helps!