LOF con fattori

La funzione lof() può accettare in input sia un data frame numerico sia una matrice di distanze per calcolare i punteggi LOF. In questo esercizio, farai pratica nel calcolare una matrice di distanze usando la distanza di Gower, che potrà poi essere passata alla funzione lof() per il calcolo dei punteggi.

Come nel precedente esercizio, i dati thyroid con le colonne di tipo carattere convertite in fattori sono già stati precaricati per te.

Questo esercizio fa parte del corso

Introduzione all'Anomaly Detection in R

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Istruzioni dell'esercizio

Calcola la matrice delle distanze di Gower per i dati thyroid e assegna il risultato al nuovo oggetto thyroid_dist.
Usa thyroid_dist per generare un LOF per ogni paziente assumendo k = 10.
Stampa l'intervallo delle distanze contenute nella matrice thyroid_dist.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Calculate Gower's distance matrix
thyroid_dist <- daisy(___, metric = ___)

# Generate LOF scores for thyroid data
thyroid_lof <- lof(thyroid_dist, k = 10)

# Range of values in the distance matrix
___(as.matrix(___))

Modifica ed esegui il codice

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: 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: Anomalie etichettate Exercise 2: Dati sulla tiroide Exercise 3: Visualizing thyroid disease Exercise 4: Punteggio di anomalia Exercise 5: Misurare le prestazioni Exercise 6: Punteggi binarizzati Exercise 7: Tabella a doppia entrata per punteggi binari Exercise 8: Precision e recall per la tiroide Exercise 9: Lavorare con le caratteristiche categoriche Exercise 10: Conversione da character a factor Exercise 11: Isolation forest con variabili categoriche Exercise 12: LOF con fattori

Esercizio in corso

Exercise 13: Riepilogo