AnomalyDetectorFunction

AnomalyDetectorFunction[…]

represents a function generated by AnomalyDetection for detecting whether data is anomalous or not.

Details and Options

Examples

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Basic Examples  (2)

Train a detector function on a numeric dataset:

Use the trained detector to find examples that are considered anomalous:

Find rarer probabilities of new examples:

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

Use the trained AnomalyDetectorFunction to find anomalies in new examples with FindAnomalies:

Find anomalies and their corresponding positions:

Scope  (1)

Train an AnomalyDetectorFunction on a list of colors:

Attempt to find outliers in a list of colors using the trained anomaly detector:

Obtain information on the trained anomaly detector:

Obtain information on training time:

Find the training method:

Options  (1)

AcceptanceThreshold  (1)

Create a dataset sampled from two different distributions:

Train an anomaly detector function:

Find anomalous/non-anomalous examples by specifying the AcceptanceThreshold:

Wolfram Research (2019), AnomalyDetectorFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html.

Text

Wolfram Research (2019), AnomalyDetectorFunction, Wolfram Language function, https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html.

CMS

Wolfram Language. 2019. "AnomalyDetectorFunction." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html.

APA

Wolfram Language. (2019). AnomalyDetectorFunction. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html

BibTeX

@misc{reference.wolfram_2024_anomalydetectorfunction, author="Wolfram Research", title="{AnomalyDetectorFunction}", year="2019", howpublished="\url{https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html}", note=[Accessed: 21-December-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_anomalydetectorfunction, organization={Wolfram Research}, title={AnomalyDetectorFunction}, year={2019}, url={https://reference.wolfram.com/language/ref/AnomalyDetectorFunction.html}, note=[Accessed: 21-December-2024 ]}