Estimate Vapnik-Chervonenkis Dimension and Sample Complexity


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Documentation for package ‘scR’ version 0.1.0

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acc_sim Utility function to generate accuracy metrics, for use with 'estimate_accuracy()'
br Replication data for 'Predicting Recidivism'
estimate_accuracy Estimate sample complexity bounds for a binary classification algorithm using either simulated or user-supplied data.
gendata Simulate data with appropriate structure to be used in estimating sample complexity bounds
getpac Recalculate achieved sample complexity bounds given different parameter inputs
loss Utility function to define the least-squares loss function to be optimized for 'simvcd()'
plot_accuracy Represent simulated sample complexity bounds graphically
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.
scb Calculate sample complexity bounds for a classifier given target accuracy
simvcd Estimate the Vapnik-Chervonenkis (VC) dimension of an arbitrary binary classification algorithm.