convert_categorical

type

the rank ordering of an ordinal variable. Users are expected to
provide a complete list of the rank ordering. Otherwise, a default rank ordering
will be used.

ordered_list

the name of the input variable. This is used to to create vectorized
input variables

var_name


Proprosess a data set. It converts categorical data into binary variables
if it is unordered or continuous variable from 0 to 1 if it is ordinal


An implementation of deep neural network with rectifier linear units trained
with stochastic gradient descent method and batch normalization. A combination of these methods have
achieved state-of-the-art performance in ImageNet classification by overcoming the gradient saturation
problem experienced by many deep architecture neural network models in the past. In addition,
batch normalization and dropout are implemented as a means of regularization. The deeplearning package is
inspired by the darch package and uses its class DArch.

convert_categorical: Data proprosess function that covnerts a categorical input to continuous input or vectorize it

Description

Usage

Arguments