Test di Grubbs

Abbiamo appena verificato che i dati sono normali. Ora applichiamo il test per outlier di Grubbs!

Il test di Grubbs valuta se il valore più distante dalla media è un outlier: può essere sia il valore massimo sia quello minimo. Il test si esegue con la funzione grubbs.test() del pacchetto outliers:

grubbs.test(x)

x sono i dati in ingresso come vettore numerico

Questo esercizio fa parte del corso

Introduzione all'Anomaly Detection in R

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Istruzioni dell'esercizio

Esegui il test di Grubbs sulle concentrazioni di nitrate.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Apply Grubbs' test to the river nitrate data
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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: Cosa intendiamo quando parliamo di anomalie?Exercise 2: Riconoscere i tipi di anomalie Exercise 3: Esplorare i dati sui nitrati del fiume Exercise 4: Mettere alla prova gli estremi con il test di Grubbs Exercise 5: Verifica visiva di normalità Exercise 6: Test di Grubbs

Esercizio in corso

Exercise 7: Caccia a più outlier con il test di Grubbs Exercise 8: Anomalie nelle serie temporali Exercise 9: Valutazione visiva della stagionalità Exercise 10: Algoritmo Seasonal Hybrid ESD Exercise 11: Interpretare l'output di Seasonal-Hybrid ESD Exercise 12: Seasonal-Hybrid ESD contro il test di Grubbs

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: Isolation trees Exercise 2: Fit and predict with an isolation tree Exercise 3: Score interpretation Exercise 4: Isolation forest Exercise 5: Fit an isolation forest Exercise 6: Checking convergence Exercise 7: Visualizing the isolation score Exercise 8: A grid of points Exercise 9: Prediction over a grid Exercise 10: Anomaly contours

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