DeleteAnomalies

DeleteAnomalies[{example1,example2,}]

gives a list in which examplei that are considered anomalous have been dropped.

DeleteAnomalies[fun,data]

drops anomalies in data using the given AnomalyDetectorFunction[] or LearnedDistribution[].

Details and Options

Examples

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Basic Examples  (3)

Drop anomalous examples in a numeric dataset:

Delete anomalous examples in a nominal dataset:

Delete anomalies from a list of colors:

Scope  (2)

Train an AnomalyDetectorFunction on a two-dimensional array of pseudorandom real numbers:

Use the trained AnomalyDetectorFunction with DeleteAnomalies to drop anomalous examples:

Obtain a random sample of training and test datasets of images:

Add anomalous examples to corrupt the datasets:

Train a distribution on images:

Use the trained distribution to drop anomalous examples in the test set:

Options  (2)

AcceptanceThreshold  (1)

Specify the AcceptanceThreshold for dropping anomalies from a list of colors:

Method  (1)

Create a dataset sampled from two different distributions:

Delete anomalies in the dataset using the "Multinormal" method:

Delete anomalies in the dataset using the "KernelDensityEstimation" method:

Applications  (4)

Find the statistical mean of numeric values with an anomaly:

Plot a list of numeric values that contains anomalies:

Plot a list of numeric values after removing anomalies:

Delete anomalies before computing the mean:

Find the linear fit for numerical data:

Delete outliers before modeling the fit:

Obtain a dataset of images:

Train an anomaly detector on the training set:

Drop anomalous examples in the test set:

Introduced in 2019
 (12.0)
 |
Updated in 2020
 (12.1)