Cost function: Difference between revisions
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In ML, cost functions are used to estimate how badly models are performing. Put simply, a cost function is a measure of how wrong the model is in terms of its ability to estimate the relationship between X and y. This cost function (you may also see this referred to as loss or error.) can be estimated by iteratively running the model to compare estimated predictions against “ground truth” — the known values of y<ref>https://towardsdatascience.com/machine-learning-fundamentals-via-linear-regression-41a5d11f5220</ref> | In ML, cost functions are used to estimate how badly models are performing. Put simply, a cost function is a measure of how wrong the model is in terms of its ability to estimate the relationship between X and y. This cost function (you may also see this referred to as loss or error.) can be estimated by iteratively running the model to compare estimated predictions against “ground truth” — the known values of y<ref>https://towardsdatascience.com/machine-learning-fundamentals-via-linear-regression-41a5d11f5220</ref> | ||
== A fairly decent video == | |||
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Revision as of 11:55, 12 March 2018
Case study notes[1]
Introduction[edit]
In ML, cost functions are used to estimate how badly models are performing. Put simply, a cost function is a measure of how wrong the model is in terms of its ability to estimate the relationship between X and y. This cost function (you may also see this referred to as loss or error.) can be estimated by iteratively running the model to compare estimated predictions against “ground truth” — the known values of y[2]
A fairly decent video[edit]