mlr_learners_surv.svm: Survival Support Vector Machine Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival Support Vector Machine Learner

Description

Survival support vector machine. Calls survivalsvm::survivalsvm() from survivalsvm.

Details

Four possible SVMs can be implemented, dependent on the type parameter. These correspond to predicting the survival time via regression (regression), predicting a continuous rank (vanbelle1, vanbelle2), or a hybrid of the two (hybrid). Whichever type is chosen determines how the crank predict type is calculated, but in any case all can be considered a valid continuous ranking.

makediff3 is recommended when using type = "hybrid".

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.svm")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “response”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalsvm

Parameters

Id	Type	Default	Levels	Range
type	character	regression	regression, vanbelle1, vanbelle2, hybrid	-
diff.meth	character	-	makediff1, makediff2, makediff3	-
gamma	numeric	NULL		(-\infty, \infty)
mu	numeric	NULL		(-\infty, \infty)
opt.meth	character	quadprog	quadprog, ipop	-
kernel	character	lin_kernel	lin_kernel, add_kernel, rbf_kernel, poly_kernel	-
kernel.pars	untyped	-		-
sgf.sv	integer	5		[0, \infty)
sigf	integer	7		[0, \infty)
maxiter	integer	20		[0, \infty)
margin	numeric	0.05		[0, \infty)
bound	numeric	10		[0, \infty)
eig.tol	numeric	1e-06		[0, \infty)
conv.tol	numeric	1e-07		[0, \infty)
posd.tol	numeric	1e-08		[0, \infty)

Prediction types

This learner returns up to two prediction types:

1. crank: a vector containing the continuous ranking scores, where each score corresponds to a specific test observation.

2. response: the survival time of each test observation, equal to -crank. This prediction type if only available for "type" equal to regression or hybrid.

Custom mlr3 parameters

• gamma, mu have replaced gamma.mu so that it's easier to tune these separately. mu is only used when type = "hybrid".

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvSVM

Methods

Public methods

• LearnerSurvSVM$new()

• LearnerSurvSVM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvSVM$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvSVM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Van Belle, Vanya, Pelckmans, Kristiaan, Van Huffel, Sabine, Suykens, AK J (2011). “Improved performance on high-dimensional survival data by application of Survival-SVM.” Bioinformatics, 27(1), 87–94.

Van Belle, Vanya, Pelckmans, Kristiaan, Van Huffel, Sabine, Suykens, AK J (2011). “Support vector methods for survival analysis: a comparison between ranking and regression approaches.” Artificial intelligence in medicine, 53(2), 107–118.

Shivaswamy, K P, Chu, Wei, Jansche, Martin (2007). “A support vector approach to censored targets.” In Seventh IEEE international conference on data mining (ICDM 2007), 655–660. IEEE.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• 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.

Examples

set.seed(123)
# Define the Learner and set parameter values
learner = lrn("surv.svm", gamma = 0.1)
print(learner)

# Define a Task
task = mlr3::tsk("rats")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.