Public methods
Method new()
creates a model
Usage
FTRL$new(
learning_rate = 0.1,
learning_rate_decay = 0.5,
lambda = 0,
l1_ratio = 1,
dropout = 0,
family = c("binomial")
)
Arguments
learning_rate
learning rate
learning_rate_decay
learning rate which controls decay. Please refer to FTRL proximal
paper for details. Usually convergense does not heavily depend on this parameter,
so default value 0.5 is safe.
lambda
regularization parameter
l1_ratio
controls L1 vs L2 penalty mixing.
1 = Lasso regression, 0 = Ridge regression. Elastic net is in between
dropout
dropout - percentage of random features to
exclude from each sample. Acts as regularization.
family
a description of the error distribution and link function to be used in
the model. Only binomial
(logistic regression) is implemented at the moment.
Method partial_fit()
fits model to the data
Usage
FTRL$partial_fit(x, y, weights = rep(1, length(y)), ...)
Arguments
x
input sparse matrix. Native format is Matrix::RsparseMatrix
.
If x
is in different format, model will try to convert it to RsparseMatrix
with as(x, "RsparseMatrix")
. Dimensions should be (n_samples, n_features)
y
vector of targets
weights
numeric vector of length `n_samples`. Defines how to amplify SGD updates
for each sample. May be useful for highly unbalanced problems.
...
not used at the moment
Method fit()
shorthand for applying `partial_fit` `n_iter` times
Usage
FTRL$fit(x, y, weights = rep(1, length(y)), n_iter = 1L, ...)
Arguments
x
input sparse matrix. Native format is Matrix::RsparseMatrix
.
If x
is in different format, model will try to convert it to RsparseMatrix
with as(x, "RsparseMatrix")
. Dimensions should be (n_samples, n_features)
y
vector of targets
weights
numeric vector of length `n_samples`. Defines how to amplify SGD updates
for each sample. May be useful for highly unbalanced problems.
n_iter
number of SGD epochs
...
not used at the moment
Method predict()
makes predictions based on fitted model
Usage
FTRL$predict(x, ...)
Arguments
x
input matrix
...
not used at the moment
Method coef()
returns coefficients of the regression model
Usage
FTRL$coef()
Method clone()
The objects of this class are cloneable with this method.
Usage
FTRL$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.