Tiroid için precision ve recall

İkili bir skor ile bilinen bir etiket arasındaki uyumu çapraz tabloyla göstermek, algoritmanın ne kadar iyi çalıştığını anlamanın harika bir yoludur. Precision ve recall ise bu tablodan türetilen ve skorun başarısı hakkında daha fazla içgörü sağlayan iki ölçüdür.

Bu egzersizde, tiroid verisini kullanarak precision ve recall'ı inceleyeceksin. Önceki egzersizlerde oluşturulan binary_lof ve binary_iso skorları, thyroid verisinde sütunlar olarak kullanıma hazır. Önceki egzersizde anlaşmaları tablolaştırmak için kullanılan kod da dahil edilmiştir.

Bu egzersiz

R ile Anomali Tespitine Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Tabulation for the binary isolation score
table(thyroid$label, thyroid$binary_iso)
# Precision for the isolation scores
precision_iso <- ___ / ___

# Tabulation for the binary lof score
table(thyroid$label, thyroid$binary_lof)
# Precision for the binary lof score
precision_lof <- ___ / ___

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Anomali Tespitine Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

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: 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: Etiketli aykırılıklar Exercise 2: Tiroid verileri Exercise 3: Tiroid hastalığını görselleştirme Exercise 4: Anomali skoru Exercise 5: Performansı ölçme Exercise 6: İkili puanlar Exercise 7: İkili puanları çapraz tabloyla göster Exercise 8: Tiroid için precision ve recall

Geçerli Egzersiz

Exercise 9: Kategorik özelliklerle çalışma Exercise 10: Character'dan factor'a dönüştürme Exercise 11: Faktörlerle isolation forest Exercise 12: Faktörlerle LOF Exercise 13: Kapanış