bm_ffnn: Fit Feedforward Neural Networks models
In vcerqueira/tsensembler: Dynamic Ensembles for Time Series Forecasting
Description
Usage
Arguments
Details
See Also
Description
Learning a Feedforward Neural Network
model from training data.
Usage
1
Arguments
form
formula
data
training data for building the predictive
model
lpars
a list containing the learning parameters
Details
Parameter setting can vary in size, maxit,
and decay parameters.
See nnet for a comprehensive description.
Imports learning procedure from nnet package.
See Also
other learning models: bm_mars;
bm_ppr; bm_gbm;
bm_glm; bm_cubist;
bm_randomforest; bm_pls_pcr;
bm_gaussianprocess; bm_svr
Other base learning models: bm_cubist,
bm_gaussianprocess, bm_gbm,
bm_glm, bm_mars,
bm_pls_pcr, bm_ppr,
bm_randomforest, bm_svr
vcerqueira/tsensembler documentation built on Oct. 28, 2020, 11:46 p.m.
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Description Usage Arguments Details See Also
Description
Learning a Feedforward Neural Network model from training data.
Usage
1 |
Arguments
form |
formula |
data |
training data for building the predictive model |
lpars |
a list containing the learning parameters |
Details
Parameter setting can vary in size, maxit, and decay parameters.
See nnet for a comprehensive description.
Imports learning procedure from nnet package.
See Also
other learning models: bm_mars;
bm_ppr; bm_gbm;
bm_glm; bm_cubist;
bm_randomforest; bm_pls_pcr;
bm_gaussianprocess; bm_svr
Other base learning models: bm_cubist,
bm_gaussianprocess, bm_gbm,
bm_glm, bm_mars,
bm_pls_pcr, bm_ppr,
bm_randomforest, bm_svr
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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