Bir nokta ızgarası

Videoda, verilerin bulunduğu yerler dışındaki konumlarda anomali puanının ne olabileceğini görmek için bir nokta ızgarası üzerinde kontur grafiği kullanmanın öğretici olabileceğini gördün.

Bu egzersizde ilgi alanı boyunca bir nokta ızgarası oluşturacak ve görselleştireceksin. Oluşturduğun ızgara, bir sonraki egzersizde tahmin edilen izolasyon puanlarını görselleştirmek için kullanılacak.

Bu egzersiz

R ile Anomali Tespitine Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Sequence of values for pH and alcohol
ph_seq <- seq(min(___),  max(___) , length.out = ___)
alcohol_seq <- seq(___)

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: 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: İzolasyon ağaçları Exercise 2: Bir isolation tree ile fit ve predict Exercise 3: Skor yorumlama Exercise 4: Isolation forest Exercise 5: Bir isolation forest modeli kur Exercise 6: Yakınsamayı kontrol etme Exercise 7: Isolation skorunu görselleştirme Exercise 8: Bir nokta ızgarası

Geçerli Egzersiz

Exercise 9: Izgarada tahmin Exercise 10: Anomali izoçizgileri

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