Seasonal-Hybrid ESD çıktısını yorumlama

Seasonal-Hybrid ESD algoritması, anomalilerin verinin içinde nerede ortaya çıktığını anlamak için kullanışlı çıktılar sağlar.

Özellikle, AnomalyDetectionVec() fonksiyonu iki öğeden oluşan bir liste döndürür:

$anoms, index ve anoms sütunlarını içeren bir veri çerçevesi (data frame)
$plot, zaman serisini anomaliler vurgulanmış şekilde gösteren bir grafik (plot = T ise)

Bu egzersiz

R ile Anomali Tespitine Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Use Seasonal-Hybrid ESD for nitrate concentrations
river_anomalies <- AnomalyDetectionVec(___)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Anomali Tespitine Giriş

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: Anomalilerden bahsederken neyi kastediyoruz?Exercise 2: Anomali türlerini tanıma Exercise 3: Nehir nitrat verilerini keşfetme Exercise 4: Aşırılıkları Grubbs testiyle sınamak Exercise 5: Normalliğin görsel kontrolü Exercise 6: Grubbs testi Exercise 7: Grubbs testiyle birden fazla aykırı değeri avlama Exercise 8: Zaman serilerinde anormallikler Exercise 9: Mevsimselliğin görsel değerlendirmesi Exercise 10: Mevsimsel Hibrit ESD algoritması Exercise 11: Seasonal-Hybrid ESD çıktısını yorumlama

Geçerli Egzersiz

Exercise 12: Seasonal-Hybrid ESD ve Grubbs testi

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