I am using the random Forrest algorithm to classify data points based upon 10 features. The accuracy score from these forests has been low (5-15% above random). I have a theory that there is a subset of highly predictable data within the dataset that shares similar features, but that the rest is essentially random - giving low accuracy meterics. How can I test this theory?

One approach I have thought of involves clustering the data and seeing if any clusters co-localize with predictions. For example, individuals within cluster X have a 95% chance of being class Y. What do you think? Thanks so much for the advice!

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    $\begingroup$ Yes using a clustering algorithm could give interesting results. But the way you define the subset may lead to biased conlusion though. Could you give more details about features and classes you use ? $\endgroup$ – Malo May 12 at 22:13

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