Classification Learner App Iterations
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Hi,
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!
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Prince Kumar
2022-4-7
Hi,
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!
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