# Factorization Machines Regression Model

`spark.fmRegressor.Rd`

`spark.fmRegressor`

fits a factorization regression model against a SparkDataFrame.
Users can call `summary`

to print a summary of the fitted model, `predict`

to make
predictions on new data, and `write.ml`

/`read.ml`

to save/load fitted models.

## Usage

```
spark.fmRegressor(data, formula, ...)
# S4 method for SparkDataFrame,formula
spark.fmRegressor(
data,
formula,
factorSize = 8,
fitLinear = TRUE,
regParam = 0,
miniBatchFraction = 1,
initStd = 0.01,
maxIter = 100,
stepSize = 1,
tol = 1e-06,
solver = c("adamW", "gd"),
seed = NULL,
stringIndexerOrderType = c("frequencyDesc", "frequencyAsc", "alphabetDesc",
"alphabetAsc")
)
# S4 method for FMRegressionModel
summary(object)
# S4 method for FMRegressionModel
predict(object, newData)
# S4 method for FMRegressionModel,character
write.ml(object, path, overwrite = FALSE)
```

## Arguments

- data
a

`SparkDataFrame`

of observations and labels for model fitting.- formula
a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.

- ...
additional arguments passed to the method.

- factorSize
dimensionality of the factors.

- fitLinear
whether to fit linear term. # TODO Can we express this with formula?

- regParam
the regularization parameter.

- miniBatchFraction
the mini-batch fraction parameter.

- initStd
the standard deviation of initial coefficients.

- maxIter
maximum iteration number.

- stepSize
stepSize parameter.

- tol
convergence tolerance of iterations.

- solver
solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".

- seed
seed parameter for weights initialization.

- stringIndexerOrderType
how to order categories of a string feature column. This is used to decide the base level of a string feature as the last category after ordering is dropped when encoding strings. Supported options are "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc". The default value is "frequencyDesc". When the ordering is set to "alphabetDesc", this drops the same category as R when encoding strings.

- object
a FM Regression Model model fitted by

`spark.fmRegressor`

.- newData
a SparkDataFrame for testing.

- path
The directory where the model is saved.

- overwrite
Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

## Value

`spark.fmRegressor`

returns a fitted Factorization Machines Regression Model.

`summary`

returns summary information of the fitted model, which is a list.

`predict`

returns the predicted values based on an FMRegressionModel.

## Note

spark.fmRegressor since 3.1.0

summary(FMRegressionModel) since 3.1.0

predict(FMRegressionModel) since 3.1.0

write.ml(FMRegressionModel, character) since 3.1.0

## Examples

```
if (FALSE) {
df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
# fit Factorization Machines Regression Model
model <- spark.fmRegressor(
df, label ~ features,
regParam = 0.01, maxIter = 10, fitLinear = TRUE
)
# get the summary of the model
summary(model)
# make predictions
predictions <- predict(model, df)
# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)
}
```