tune.maxent: fits a maximum entropy model given a training matrix and a...

In maxent: Low-memory Multinomial Logistic Regression with Support for Text Classification

Description

Fits a multinomial logistic regression model of class maxent-class given a matrix or matrix.csr with training data, and a vector or factor with corresponding labels.

Usage

tune.maxent(feature_matrix, code_vector, nfold=3, showall=FALSE, verbose=FALSE)

Arguments

feature_matrix	A DocumentTermMatrix or TermDocumentMatrix (package tm), Matrix (package Matrix), matrix.csr (SparseM), data.frame, or matrix.
code_vector	A factor or vector of labels corresponding to each document in the feature_matrix.
nfold	An integer specifying the number of folds to perform for cross-validation. Defaults to 3.
showall	A logical specifying whether to show the accuracy results of all tested parameter configurations. Defaults to FALSE.
verbose	A logical specifying whether to provide descriptive output about the fitting process. Defaults to FALSE, or no output.

Value

Returns an object of class matrix with configurations along the y-axis and parameters along the x-axis.

Author(s)

Timothy P. Jurka <[e-mail]>

Examples

# LOAD LIBRARY
library(maxent)

# A DIFFERENT EXAMPLE
data(iris)
attach(iris)

x <- subset(iris, select = -Species)
y <- Species

f <- tune.maxent(x,y,nfold=3,showall=TRUE)

Example output

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

Loading required package: tm
Loading required package: NLP

maxent documentation built on May 29, 2017, 1:40 p.m.