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I am using sklearn's Isolation Forest as a model to detect anomalies. My dataset is relatively small, 50 records with only 2-3 features.

To prevent any overfitting, what would you recommend to tune the model. Additionally, given how small the dataset is, would IF still be an appropriate choice.

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    $\begingroup$This question is similar to: Unsupervised Isolation Forrest sklearn hyperparameters. If you believe it’s different, please edit the question, make it clear how it’s different and/or how the answers on that question are not helpful for your problem.$\endgroup$
    – Guna
    CommentedApr 21 at 18:47

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