risk_bounds

Utility function to generate data points for estimation of the VC Dimension of a user-specified binary classification algorithm given a specified sample size.

We provide a suite of tools for estimating the sample complexity of a chosen model through theoretical bounds and simulation. The package incorporates methods for estimating the Vapnik-Chervonenkis dimension (VCD) of a chosen algorithm, which can be used to estimate its sample complexity. Alternatively, we provide simulation methods to estimate sample complexity directly. For more details, see Carter, P & Choi, D (2024). "Learning from Noise: Applying Sample Complexity for Political Science Research" <doi:10.31219/osf.io/evrcj>.

Perry Carter

Estimate Vapnik-Chervonenkis Dimension and Sample Complexity

Dahyun Choi

risk_bounds function

<dl><dt>x</dt>
<dd>An integer giving the desired sample size for which the target function is to be approximated.</dd>
<dt>...</dt>
<dd>Additional model parameters to be specified by the user.</dd></dl>

Arguments

Utility function to generate data points for estimation of the VC Dimension of a user-specified binary classification algorithm given a specified sample size. — risk_bounds

<dl>

<dt>x</dt>
<dd>An integer giving the desired sample size for which the target function is to be approximated.</dd>


<dt>...</dt>
<dd>Additional model parameters to be specified by the user.</dd>

</dl>

Utility function to generate data points for estimation of the VC Dimension of a user-specified binary classification algorithm given a specified sample size.

risk_bounds: Utility function to generate data points for estimation of the VC Dimension of a user-specified binary classification algorithm given a specified sample size.

Description

Usage

Value

Arguments