Description Usage Arguments References See Also Examples
KnoFM.train
is a method training a knowledge-extracting Factorization Machine.
1 | KnoFM.train(data, target, multicore = T, silent = F)
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data |
an object of class |
target |
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multicore |
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silent |
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[1] J. Knoll , J. Stuebinger, and M. Grottke, Exploiting social media with higher-order Factorization Machines: Statistical arbitrage on high-frequency data of the S&P 500. FAU Discussion Papers in Economics, University of Erlangen-Nuernberg, 2017.
[2] J. Knoll, Recommending with Higer-Order Factorization Machines, Research and Development in Intelligent Systems XXXIII, 2016.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ## Not run:
### Example to illustrate the usage of the method
### Data set very small and not sparse, results not representative
### Please study major example in general help 'FactoRizationMachines'
# Load data set
library(FactoRizationMachines)
library(MASS)
data("Boston")
# Subset data to training and test data
set.seed(123)
subset=sample.int(nrow(Boston),nrow(trees)*.8)
data.train=Boston[subset,-ncol(Boston)]
target.train=Boston[subset,ncol(Boston)]
data.test=Boston[-subset,-ncol(Boston)]
target.test=Boston[-subset,ncol(Boston)]
# Predict with an adaptive-order Factorization Machine
# using one CPU core and printing progress
model=KnoFM.train(data.train,target.train,FALSE,FALSE)
# RMSE resulting from test data prediction
sqrt(mean((predict(model,data.test)-target.test)^2))
## End(Not run)
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