Verifica della convergenza

Il punteggio di anomalia da un isolation forest di solito non cambia dopo che è stato fatto crescere un certo numero di alberi. Questo si chiama convergenza e può essere verificato confrontando i punteggi generati da foreste con numeri diversi di alberi. Se i punteggi differiscono molto, potrebbe voler dire che servono più alberi.

In questo esercizio, i punteggi per isolation forest con diversi numeri di alberi sono già stati calcolati per te e sono contenuti nel data frame wine_scores.

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Introduzione all'Anomaly Detection in R

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# View the contents of the wine scores
head(wine_scores)

# Scatterplot of 2000 trees vs 1000 trees scores
plot(___, ___)

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Questo esercizio fa parte del corso

Introduzione all'Anomaly Detection in R

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In this chapter, you'll learn how numerical and graphical summaries can be used to informally assess whether data contain unusual points. You'll use a statistical procedure called Grubbs' test to check whether a point is an outlier, and learn about the Seasonal-Hybrid ESD algorithm, which can help identify outliers when the data are a time series.

Exercise 1: What do we mean when we talk about anomalies?Exercise 2: Recognizing anomaly types Exercise 3: Exploring the river nitrate data Exercise 4: Testing the extremes with Grubbs' test Exercise 5: Visual check of normality Exercise 6: Grubbs' test Exercise 7: Hunting multiple outliers using Grubbs' test Exercise 8: Anomalies in time series Exercise 9: Visual assessment of seasonality Exercise 10: Seasonal Hybrid ESD algorithm Exercise 11: Interpreting Seasonal-Hybrid ESD output Exercise 12: Seasonal-Hybrid ESD versus Grubbs' test

In this chapter, you'll learn how to calculate the k-nearest neighbors distance and the local outlier factor, which are used to construct continuous anomaly scores for each data point when the data have multiple features. You'll learn the difference between local and global anomalies and how the two algorithms can help in each case.

Exercise 1: k-nearest neighbors distance score Exercise 2: Exploring wine Exercise 3: kNN distance matrix Exercise 4: kNN distance score Exercise 5: Visualizing kNN distance Exercise 6: Standardizing features Exercise 7: Appending the kNN score Exercise 8: Visualizing kNN distance score Exercise 9: Local outlier factor Exercise 10: LOF calculation Exercise 11: LOF visualization Exercise 12: LOF vs kNN

k-nearest neighbors distance and local outlier factor use the distance or relative density of the nearest neighbors to score each point. In this chapter, you'll explore an alternative tree-based approach called an isolation forest, which is a fast and robust method of detecting anomalies that measures how easily points can be separated by randomly splitting the data into smaller and smaller regions.

Exercise 1: Alberi di isolamento Exercise 2: Esegui il fit e prevedi con un isolation tree Exercise 3: Interpretazione dei punteggi Exercise 4: Isolation forest Exercise 5: Adatta un isolation forest Exercise 6: Verifica della convergenza

Esercizio in corso

Exercise 7: Visualizzare l'isolation score Exercise 8: Una griglia di punti Exercise 9: Predizione su una griglia Exercise 10: Curve di anomalia

You've now been introduced to a few different algorithms for anomaly scoring. In this final chapter, you'll learn to compare the detection performance of the algorithms in instances where labeled anomalies are available. You'll learn to calculate and interpret the precision and recall statistics for an anomaly score, and how to adapt the algorithms so they can accommodate data with categorical features.

Exercise 1: Labeled anomalies Exercise 2: Thyroid data Exercise 3: Visualizing thyroid disease Exercise 4: Anomaly score Exercise 5: Measuring performance Exercise 6: Binarized scores Exercise 7: Cross-tabulate binary scores Exercise 8: Thyroid precision and recall Exercise 9: Working with categorical features Exercise 10: Converting character to factor Exercise 11: Isolation forest with factors Exercise 12: LOF with factors Exercise 13: Wrap-up