R: BNS

BNS {text2vec} | R Documentation

## BNS

### Description

Creates BNS (bi-normal separation) model. Defined as: Q(true positive rate) - Q(false positive rate), where Q is a quantile function of normal distribution.

### Usage

    BNS

### Format

`R6Class` object.

### Details

Bi-Normal Separation

### Fields

`bns_stat`

  `data.table` with computed BNS statistic. Useful for feature selection.

### Usage

For usage details see Methods, Arguments and Examples sections.

    bns = BNS$new(treshold = 0.0005)
    bns$fit_transform(x, y)
    bns$transform(x)

### Methods

`$new(treshold = 0.0005)`

  Creates bns model

`$fit_transform(x, y)`

  fit model to an input sparse matrix (preferably in "dgCMatrix" format) and then transforms it.

`$transform(x)`

  transform new data `x` using bns from train data

### Arguments

bns

  A `BNS` object

x

  An input document term matrix. Preferably in `dgCMatrix` format

y

  Binary target variable coercible to logical.

treshold

  Clipping treshold to avoid infinities in quantile function.

### Examples

    data("movie_review")
    N = 1000
    it = itoken(head(movie_review$review, N), preprocessor = tolower, tokenizer = word_tokenizer)
    vocab = create_vocabulary(it)
    dtm = create_dtm(it, vocab_vectorizer(vocab))
    model_bns = BNS$new()
    dtm_bns = model_bns$fit_transform(dtm, head(movie_review$sentiment, N))

[Package text2vec version 0.5.1 Index]