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DeleteAnomalies
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DeleteAnomalies

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DeleteAnomalies[{example1,example2,…}]

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

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DeleteAnomalies[fun,data]

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

Details and Options

Examples

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Basic Examples  (3)Summary of the most common use cases

Drop anomalous examples in a numeric dataset:

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In[6]:=6
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https://wolfram.com/xid/0hyvacb2y58c5q74-5hhlm7
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Out[6]=6

Delete anomalous examples in a nominal dataset:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-1giaj9
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Out[1]=1

Delete anomalies from a list of colors:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-w1cs1m
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Out[1]=1

Scope  (3)Survey of the scope of standard use cases

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

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-9eb8g
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Out[1]=1

Use the trained AnomalyDetectorFunction with DeleteAnomalies to drop anomalous examples:

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In[3]:=3
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https://wolfram.com/xid/0hyvacb2y58c5q74-mdwhj2
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Out[3]=3

Delete anomalies from a Tabular object:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-87lln3
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Out[1]=1

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

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-2v5gze
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Out[1]=1

Add anomalous examples to corrupt the datasets:

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In[2]:=2
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https://wolfram.com/xid/0hyvacb2y58c5q74-59fu7o
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Out[2]=2

Train a distribution on images:

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In[3]:=3
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https://wolfram.com/xid/0hyvacb2y58c5q74-40o90t
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Out[3]=3

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

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In[4]:=4
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https://wolfram.com/xid/0hyvacb2y58c5q74-u2zvha
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Out[4]=4

Options  (2)Common values & functionality for each option

AcceptanceThreshold  (1)

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

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-y8b47x
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Out[1]=1

Method  (1)

Create a dataset sampled from two different distributions:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-lrvca8
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Out[1]=1

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

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In[2]:=2
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https://wolfram.com/xid/0hyvacb2y58c5q74-hwhlow
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Out[2]=2

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

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In[3]:=3
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https://wolfram.com/xid/0hyvacb2y58c5q74-7clzek
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Out[3]=3

Applications  (4)Sample problems that can be solved with this function

Find the statistical mean of numeric values with an anomaly:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-wkmvw0
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Out[1]=1

Delete anomalies before computing the mean:

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In[2]:=2
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https://wolfram.com/xid/0hyvacb2y58c5q74-moihc5
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Out[2]=2

Plot a list of numeric values that contains anomalies:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-2xs34q
Direct link to example
Out[1]=1

Plot a list of numeric values after removing anomalies:

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In[2]:=2
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https://wolfram.com/xid/0hyvacb2y58c5q74-q99u9i
Direct link to example
Out[2]=2

Find the linear fit for numerical data:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-fr8gq2
Direct link to example
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In[2]:=2
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https://wolfram.com/xid/0hyvacb2y58c5q74-ito2gu
Direct link to example
Out[2]=2

Delete outliers before modeling the fit:

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In[3]:=3
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https://wolfram.com/xid/0hyvacb2y58c5q74-hamet6
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Out[3]=3

Obtain a dataset of images:

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In[1]:=1
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https://wolfram.com/xid/0hyvacb2y58c5q74-hamat6
Direct link to example
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In[2]:=2
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https://wolfram.com/xid/0hyvacb2y58c5q74-qzzjzs
Direct link to example
Out[2]=2

Train an anomaly detector on the training set:

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In[3]:=3
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https://wolfram.com/xid/0hyvacb2y58c5q74-xk37in
Direct link to example
Out[3]=3

Drop anomalous examples in the test set:

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In[4]:=4
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https://wolfram.com/xid/0hyvacb2y58c5q74-usfnwe
Direct link to example
Out[4]=4
Wolfram Research (2019), DeleteAnomalies, Wolfram Language function, https://reference.wolfram.com/language/ref/DeleteAnomalies.html (updated 2025).
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Wolfram Research (2019), DeleteAnomalies, Wolfram Language function, https://reference.wolfram.com/language/ref/DeleteAnomalies.html (updated 2025).

Text

Wolfram Research (2019), DeleteAnomalies, Wolfram Language function, https://reference.wolfram.com/language/ref/DeleteAnomalies.html (updated 2025).

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Wolfram Research (2019), DeleteAnomalies, Wolfram Language function, https://reference.wolfram.com/language/ref/DeleteAnomalies.html (updated 2025).

CMS

Wolfram Language. 2019. "DeleteAnomalies." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/DeleteAnomalies.html.

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Wolfram Language. 2019. "DeleteAnomalies." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/DeleteAnomalies.html.

APA

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

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

BibTeX

@misc{reference.wolfram_2025_deleteanomalies, author="Wolfram Research", title="{DeleteAnomalies}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/DeleteAnomalies.html}", note=[Accessed: 26-March-2025 ]}

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@misc{reference.wolfram_2025_deleteanomalies, author="Wolfram Research", title="{DeleteAnomalies}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/DeleteAnomalies.html}", note=[Accessed: 26-March-2025 ]}

BibLaTeX

@online{reference.wolfram_2025_deleteanomalies, organization={Wolfram Research}, title={DeleteAnomalies}, year={2025}, url={https://reference.wolfram.com/language/ref/DeleteAnomalies.html}, note=[Accessed: 26-March-2025 ]}

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@online{reference.wolfram_2025_deleteanomalies, organization={Wolfram Research}, title={DeleteAnomalies}, year={2025}, url={https://reference.wolfram.com/language/ref/DeleteAnomalies.html}, note=[Accessed: 26-March-2025 ]}