I'm using IsolationForest algorithm in order to detect anomalies in my data and to use this model to detect future anomalies in new rows and came across a few questions:
Is the model good for predicting new data or only on data that the algorithm was trained on and why?
If the training set had a feature with 0 variance, and now I receive a row with a different value on that feature, how would the model detect it? (for example, all the training data rows have the same country, US, as a feature and now comes a new row from Japan), To my understanding the tree will not separate on a feature with 0 variance thus the model will not find anomalies on this feature (which seems very odd to me)
If IsolationForest is not good at learning on data that can have 0 variance in some columns, would you recommend another algorithm that will capture such cenarios?