Hands-On Data Analysis with Pandas

Comparing models

LOF indicates fewer outliers than the isolation forest, but perhaps they don't even agree with each other. As we learned in Chapter 10, Making Better Predictions – Optimizing Models, we can use the cohen_kappa_score() function from sklearn.metrics to check their level of agreement:

>>> from sklearn.metrics import cohen_kappa_score>>> is_lof_outlier = np.where(...     lof_preds < lof_pipeline.named_steps['lof'].offset_, ...     'outlier', 'inlier'... )>>> is_iso_outlier = np.where(...     isolation_forest_preds == -1, 'outlier', 'inlier'... )>>> cohen_kappa_score(is_lof_outlier, is_iso_outlier)0.012896350639585386

They agree only on 1.3% of the data points, indicating it's not so obvious which data points are anomalies. Without labeled ...