I have a training dataset with 140 000 instances with 140 features. Due to the scale of the dataset I'm experiment with letting a model do the anomaly detection and have tried LocalOutlierFactor and IsolationForest. I did not performed any hyperparameter tuning, just executed the functions from sklearn.
lof=LocalOutlierFactor() yhat=lof.fit_predict(features) isofor=IsolationForest(n_estimators=100).fit(features) isolated_instances = iso_f.predict(features)
LOF identified 13 000 outliers and Isolation Forest found 4 000. This discrepancy is of course something I started to think about. Since I'm new to this field of machine learning I wonder how I should think when I fit an anomaly detection model. Is it something I should bear in mind? How can I asses the performance when it's unsupervised? Is it if prediction on test set further down the road is improving? Or is there other methods?