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.
