| One-Class SVM Transformation |
Description | - One-Class Support Vector Machine (One Class SVM) is an unsupervised variation of SVM used for anomaly detection.
- One-Class SVM is an unsupervised algorithm for outlier detection. It detects whether a new data is similar to or different from the data on which it is trained.
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Why to use | To identify whether a data point is an inlier (belonging to the distribution) or an outlier (not belonging to the distribution) |
When to use | When the independent variable is numerical when | When not to use | In the case of non-continuous, categorical, or textual variables |
Prerequisites | - The independent variable should contain any integer or float numerical value. It should not possess any infinite or missing value.
- You can apply One-Class SVM to a dataset with or without applying the Train-Test Split
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Input | Non-clustered data | Output | Clustered data |
Statistical Methods Used | - Accuracy
- F-score
- Sensitivity
- Specificity
- Confusion Matrix
| Limitations | - It is a part of novelty detection (and not outlier detection)
- It performs better for small samples but underperforms for large samples
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