Regression Stochastic Gradient Descent Learner
mlr_learners_regr.sgd.Rd
Stochastic Gradient Descent for learning various linear models.
Calls RWeka::make_Weka_classifier()
from RWeka.
Initial parameter values
F
:Has only 3 out of 5 original loss functions: 2 = squared loss (regression), 3 = epsilon insensitive loss (regression) and 4 = Huber loss (regression) with 2 (squared loss) being the new default
Reason for change: this learner should only contain loss functions appropriate for regression tasks
Custom mlr3 parameters
output_debug_info
:original id: output-debug-info
do_not_check_capabilities
:original id: do-not-check-capabilities
num_decimal_places
:original id: num-decimal-places
batch_size
:original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
Parameters
Id | Type | Default | Levels | Range |
subset | untyped | - | - | |
na.action | untyped | - | - | |
F | character | 2 | 2, 3, 4 | - |
L | numeric | 0.01 | \((-\infty, \infty)\) | |
R | numeric | 1e-04 | \((-\infty, \infty)\) | |
E | integer | 500 | \((-\infty, \infty)\) | |
C | numeric | 0.001 | \((-\infty, \infty)\) | |
N | logical | - | TRUE, FALSE | - |
M | logical | - | TRUE, FALSE | - |
S | integer | 1 | \((-\infty, \infty)\) | |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
do_not_check_capabilities | logical | FALSE | TRUE, FALSE | - |
num_decimal_places | integer | 2 | \([1, \infty)\) | |
batch_size | integer | 100 | \([1, \infty)\) | |
options | untyped | NULL | - |
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrSGD
Examples
# Define the Learner
learner = mlr3::lrn("regr.sgd")
print(learner)
#>
#> ── <LearnerRegrSGD> (regr.sgd): Stochastic Gradient Descent ────────────────────
#> • Model: -
#> • Parameters: F=2
#> • Packages: mlr3 and RWeka
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties:
#> • Other settings: use_weights = 'error'
# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> Loss function: Squared loss (linear regression)
#>
#> mpg =
#>
#> 3.9265 (normalized) am
#> + -4.7171 (normalized) carb
#> + 1.0438 (normalized) cyl
#> + -0.7392 (normalized) disp
#> + 4.6274 (normalized) drat
#> + 0.5755 (normalized) gear
#> + -4.4774 (normalized) hp
#> + 6.4483 (normalized) qsec
#> + 0.5592 (normalized) vs
#> + -3.7082 (normalized) wt
#> + 17.5311
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 7.613838