Outlier detection with several methods. — scikit-learn 0.19.2 documentation
Outlier detection with several methods.¶ When the amount of contamination is known, this example illustrates three different ways of performing Novelty and Outlier Detection: based on a robust estimator of covariance, which is assuming that the data are Gaussian distributed and performs better than the One-Class SVM in that case. using the One-Class SVM and its ability to capture the shape of the
One-class SVM with non-linear kernel (RBF)
Note Go to the end to download the full example code or to run this example in your browser via JupyterLite or Binder One-class SVM with non-linear kernel (RBF)¶ An example using a one-class SVM for novelty detection. One-class SVM is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set. /home/circleci/pro
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