Cost function (machine learning)
Last revised by Matt Adams ◉
on 12 May 2019
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Citation:
Adams M, Murphy A, Cost function (machine learning). Reference article, Radiopaedia.org (Accessed on 03 Dec 2024) https://doi.org/10.53347/rID-56153
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56153
Article created:
15 Oct 2017,
Matt Adams ◉
Disclosures:
At the time the article was created Matt Adams had no recorded disclosures.
View Matt Adams's current disclosures
Last revised:
12 May 2019,
Matt Adams ◉
Disclosures:
At the time the article was last revised Matt Adams had no recorded disclosures.
View Matt Adams's current disclosures
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4 times, by
2 contributors -
see full revision history and disclosures
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A cost function is a mechanism utilised in supervised machine learning, the cost function returns the error between predicted outcomes compared with the actual outcomes. The aim of supervised machine learning is to minimise the overall cost, thus optimising the correlation of the model to the system that it is attempting to represent.
NB loss function is defined as the error for one sample, whereas the cost function is the average loss across a number of samples in a given dataset.
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References
- 1. Nikhil Buduma, Nicholas Locascio. Fundamentals of Deep Learning. ISBN: 9781491925614
- 2. Stephen Marsland. Machine Learning. ISBN: 9781498759786
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