find.CV.C: K-fold cross-validation to determine optimal tuning parameter

In textreg: n-Gram Text Regression, aka Concise Comparative Summarization

Description

Given a corpus, divide into K-folds and do test-train spilts averaged over the folds.

Usage

find.CV.C(corpus, labeling, banned, K = 5, length.out = 10,
  max_C = NULL, verbose = FALSE, ...)

Arguments

corpus	The text
labeling	The labeling
banned	The words to drop.
K	Number of folds for K-fold cross-validation
length.out	number of values of C to examine from 0 to max_C.
max_C	upper bound for tuning parameter; if NULL, sets max_C to threshold C
verbose	Print progress
...	parameters to be passed to the original textreg() function

Details

Increments tuning parameter, performs K-fold cross-validation on each C giving a profile of predictive power for different C.

Value

a dataframe containing the mean/standard error of out-of-sample predictions under K-Fold Cross-validation

See Also

make.CV.chart

textreg documentation built on May 2, 2019, 8:34 a.m.