EZtune

EZtune-package

The EZtune package contains two functions and four datasets. The functions
are eztune and eztune.cv. The datasets are lichen, lichenTest, mullein, and
mulleinTest

Contains two functions that are intended to make
tuning supervised learning methods easy. The eztune function uses a
genetic algorithm or quasi-Newton-Raphson optimizer to find the best
set of tuning parameters. The user can choose the optimizer, the
learning method, and if optimization will be based on accuracy
obtained through resubstitution or cross-validation. The function
eztune.cv will compute a cross-validated error rate. The purpose
of eztune.cv is to provide a cross-validated accuracy when resubstitution
is used for optimization because resubstitution
typically produces an accuracy that is too high.

Jill Lundell

Tunes AdaBoost, Support Vector Machines, and Gradient Boosting
Machines

EZtune-package function

The EZtune functions are eztune and eztune.cv. The function eztune will
autotune an adaboost, gradient boosting machine, and support vector
machine using a genetic algorithm or a quasi-Newton-Raphson optimizer.
The eztune.cv function returns the cross-validated error for an object
produced by eztune.

Eztune functions

The lichen dataset has 840 observations and 40 variables. Seven of the
variables are binary response variables. The lichenTest dataset is a test
dataset for the lichen dataset that consists of 300 observations and
the same 40 variables.
The mullein dataset consists of 12094 observations and 32 variables. One
variable is a binary response. The mulleinTest dataset is a test
dataset for the mullein data that consists of 1512 observations
and the same 32 variables as mullein.

EZtune-package: Tuning supervised learning models.

Description

Arguments

Eztune functions

EZtune datasets