Empirical Sample Complexity Bounds

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Documentation for package ‘scR’ version 0.7.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

acc_sim	Utility function to generate accuracy metrics, for use with 'estimate_accuracy()'
conduct_interpolation	Conduct interpolation on a single simulation
create_scb_model	Create custom model fitting function
create_scb_prediction	Create custom prediction function
estimate_accuracy	Estimate sample complexity bounds for a binary classification algorithm using either simulated or user-supplied data.
fit_and_predict	Fit an extrapolation model using nonlinear least squares
fit_gp_scb_curve	Fit a monotone Gaussian process sample-complexity curve
gendata	Simulate data with appropriate structure to be used in estimating sample complexity bounds
getpac	Recalculate achieved sample complexity bounds given different parameter inputs
interpolate_scb	Conduct interpolation on a list of data
interpolate_scb_gp	Interpolate sample-complexity curves using monotone Gaussian processes
loss	Utility function to define the least-squares loss function to be optimized for 'simvcd()'
plot.empirical_scb_gp	Plot a monotone Gaussian process sample-complexity fit
plot.empirical_scb_list	Plot method for an 'empirical_scb_list' object
plot.scb_data	Plot method for simulated sample complexity bounds ('scb_data' object)
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.
summary.empirical_scb_gp	Summarize a monotone Gaussian process sample-complexity fit
summary.empirical_scb_list	Summary of empirical sample complexity bound results