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Outlier and Novelty Detection in Practice

bookChoosing the Right Method

Choosing the right outlier or novelty detection method depends on your data and goals:

  • Dimensionality: For low-dimensional, well-structured data, use classical statistical methods like robust covariance or Mahalanobis distance;
  • High-dimensional or nonlinear data: Prefer tree-based methods such as Isolation Forest or boundary-based methods like One-Class SVM;
  • Clusters or varying densities: Density-based methods such as Local Outlier Factor (LOF) are often more effective.

Also consider your objectives:

  • If you need clear explanations for flagged cases, choose simpler models or those with interpretable decision boundaries;
  • For high contamination rates, select robust methods that do not assume most data is normal;
  • Decide whether you need novelty detection (finding new patterns) or outlier detection (flagging rare cases), as some algorithms are better suited for one or the other.
Note
Note

Key considerations for selecting a detection method:

  • Data shape: assess dimensionality and distribution;
  • Contamination: estimate the expected proportion of anomalies;
  • Interpretability: determine how important it is to explain decisions to stakeholders.
question mark

You have a high-dimensional credit card transaction dataset with few fraudulent cases. You want to flag suspicious transactions and provide explanations for each. Which detection method is best?

Select the correct answer

Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 6. Kapitel 3

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bookChoosing the Right Method

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Choosing the right outlier or novelty detection method depends on your data and goals:

  • Dimensionality: For low-dimensional, well-structured data, use classical statistical methods like robust covariance or Mahalanobis distance;
  • High-dimensional or nonlinear data: Prefer tree-based methods such as Isolation Forest or boundary-based methods like One-Class SVM;
  • Clusters or varying densities: Density-based methods such as Local Outlier Factor (LOF) are often more effective.

Also consider your objectives:

  • If you need clear explanations for flagged cases, choose simpler models or those with interpretable decision boundaries;
  • For high contamination rates, select robust methods that do not assume most data is normal;
  • Decide whether you need novelty detection (finding new patterns) or outlier detection (flagging rare cases), as some algorithms are better suited for one or the other.
Note
Note

Key considerations for selecting a detection method:

  • Data shape: assess dimensionality and distribution;
  • Contamination: estimate the expected proportion of anomalies;
  • Interpretability: determine how important it is to explain decisions to stakeholders.
question mark

You have a high-dimensional credit card transaction dataset with few fraudulent cases. You want to flag suspicious transactions and provide explanations for each. Which detection method is best?

Select the correct answer

Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 6. Kapitel 3
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