Choosing the Right Detection Technique
With multiple approaches to outlier and novelty detection available in scikit-learn, selecting the right method depends on the nature of your dataset, the presence or absence of labels, and the use case. In this section, we’ll compare the key characteristics of various detection algorithms covered in this chapter and provide practical guidance to help you determine which approach best suits your needs.n this recipe we'll explore decision criteria including assumptions about the data distribution, scalability, dimensionality, interpretability, and whether the model supports predictions on new, unseen data.
Getting ready
We’ll summarize our experimental setup and reuse model evaluation results from earlier sections. (NOTE: This part is really OPTIONAL).
Load the libraries:
import pandas as pd from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor from sklearn.svm import OneClassSVMCreate...
