radiant.model (version 1.6.7)

nn: Neural Networks using nnet

Description

Neural Networks using nnet

Usage

nn(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  size = 1,
  decay = 0.5,
  wts = "None",
  seed = NA,
  check = "standardize",
  form,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Value

A list with all variables defined in nn as an object of class nn

Arguments

dataset

Dataset

rvar

The response variable in the model

evar

Explanatory variables in the model

type

Model type (i.e., "classification" or "regression")

lev

The level in the response variable defined as _success_

size

Number of units (nodes) in the hidden layer

decay

Parameter decay

wts

Weights to use in estimation

seed

Random seed to use as the starting point

check

Optional estimation parameters ("standardize" is the default)

form

Optional formula to use instead of rvar and evar

data_filter

Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")

arr

Expression to arrange (sort) the data on (e.g., "color, desc(price)")

rows

Rows to select from the specified dataset

envir

Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/model/nn.html for an example in Radiant

See Also

summary.nn to summarize results

plot.nn to plot results

predict.nn for prediction

Examples

nn(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
nn(titanic, "survived", c("pclass", "sex")) %>% str()
nn(diamonds, "price", c("carat", "clarity"), type = "regression") %>% summary()