avNNet.default
From caret v6.070
by Max Kuhn
Neural Networks Using Model Averaging
Aggregate several neural network models
 Keywords
 neural
Usage
"avNNet"(x, y, repeats = 5, bag = FALSE, allowParallel = TRUE, seeds = sample.int(1e+05, repeats), ...)
"avNNet"(formula, data, weights, ..., repeats = 5, bag = FALSE, allowParallel = TRUE, seeds = sample.int(1e+05, repeats), subset, na.action, contrasts = NULL)
"predict"(object, newdata, type = c("raw", "class", "prob"), ...)
Arguments
 formula

A formula of the form
class ~ x1 + x2 + ...
 x

matrix or data frame of
x
values for examples.  y
 matrix or data frame of target values for examples.
 weights
 (case) weights for each example  if missing defaults to 1.
 repeats
 the number of neural networks with different random number seeds
 bag
 a logical for bagging for each repeat
 seeds
 random number seeds that can be set prior to bagging (if done) and network creation. This helps maintain reproducibility when models are run in parallel.
 allowParallel
 if a parallel backend is loaded and available, should the function use it?
 data

Data frame from which variables specified in
formula
are preferentially to be taken.  subset
 An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
 na.action

A function to specify the action to be taken if
NA
s are found. The default action is for the procedure to fail. An alternative isna.omit
, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)  contrasts
 a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
 object

an object of class
avNNet
as returned byavNNet
.  newdata
 matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case.
 type

Type of output, either:
raw
for the raw outputs,code
for the predicted class orprob
for the class probabilities.  ...

arguments passed to
nnet
Details
Following Ripley (1996), the same neural network model is fit using different random number seeds. All the resulting models are used for prediction. For regression, the output from each network are averaged. For classification, the model scores are first averaged, then translated to predicted classes. Bagging can also be used to create the models.
If a parallel backend is registered, the foreach package is used to train the networks in parallel.
Value

For
 model
 a list of the models generated from
nnet
 repeats
 an echo of the model input
 names
 if any predictors had only one distinct value, this is a character string of the remaining columns. Otherwise a value of
NULL
avNNet
, an object of "avNNet"
or "avNNet.formula"
. Items of interest in the output are:
References
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.
See Also
Examples
data(BloodBrain)
## Not run:
# modelFit < avNNet(bbbDescr, logBBB, size = 5, linout = TRUE, trace = FALSE)
# modelFit
#
# predict(modelFit, bbbDescr)
# ## End(Not run)
Community examples
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