Description Usage Arguments See Also Examples
Function for predicting new data based on a FMmodel object
1 2 |
object |
a FMmodel object (output of |
newdata |
new data for prediction based on the FMmodel object (number of features must match the features of the training data) |
truncate |
bool indicating whether the output should be trunceted ( |
... |
additional arguments |
SVM.train
,
FM.train
,
HoFM.train
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ### 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 10 second-order and 5 third-order factor
model=HoFM.train(data.train,target.train)
# RMSE resulting from test data prediction
sqrt(mean((predict(model,data.test)-target.test)^2))
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