Features standaardiseren

Het is belangrijk om de invoerkenmerken voor de kNN-afstandsberekening te standaardiseren met de functie scale(). Standaardisatie zorgt ervoor dat features met een grote gemiddelde waarde of variantie niet onevenredig veel invloed hebben op de kNN-afstandsscore.

Deze oefening maakt deel uit van de cursus

Introductie tot anomaliedetectie in R

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Oefeninstructies

Pas de functie summary() toe op de wine-gegevens om de gemiddelde, minimum- en maximumwaarden voor pH en alcohol te berekenen.
Gebruik de functie scale() om een gestandaardiseerde versie van de wine-gegevens te maken, genaamd wine_scaled.
Gebruik de functie summary() op wine_scaled om te controleren dat het gemiddelde en de bereiken zijn veranderd.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Without standardization, features have different scales
summary(wine)

# Standardize the wine columns
wine_scaled <- ___

# Standardized features have similar means and quartiles
___(___)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot anomaliedetectie in R

SkillTag.level.intermediateSkillTag.label

4.8+

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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-afstandsscore Exercise 2: Wijn verkennen Exercise 3: kNN-afstandsmatrix Exercise 4: kNN-afstandsscore Exercise 5: kNN-afstand visualiseren Exercise 6: Features standaardiseren

Huidige oefening

Exercise 7: De kNN-score toevoegen Exercise 8: kNN-afstandsscore visualiseren Exercise 9: Local outlier factor Exercise 10: LOF-berekening Exercise 11: LOF-visualisatie 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