Quadratic Discriminant Analysis Classification Learner
Source:R/LearnerClassifQDA.R
mlr_learners_classif.qda.Rd
Quadratic discriminant analysis.
Calls MASS::qda()
from package MASS.
Details
Parameters method
and prior
exist for training and prediction but
accept different values for each. Therefore, arguments for
the predict stage have been renamed to predict.method
and predict.prior
,
respectively.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("classif.qda")
mlr_learnerslrn("classif.qda")
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3learners, MASS
Parameters
Id | Type | Default | Levels | Range |
method | character | moment | moment, mle, mve, t | - |
nu | integer | - | \((-\infty, \infty)\) | |
predict.method | character | plug-in | plug-in, predictive, debiased | - |
predict.prior | untyped | - | - | |
prior | untyped | - | - |
References
Venables WN, Ripley BD (2002). Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, http://www.stats.ox.ac.uk/pub/MASS4/.
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
Package mlr3extralearners for more learners.
Dictionary of Learners: mlr_learners
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_classif.cv_glmnet
,
mlr_learners_classif.glmnet
,
mlr_learners_classif.kknn
,
mlr_learners_classif.lda
,
mlr_learners_classif.log_reg
,
mlr_learners_classif.multinom
,
mlr_learners_classif.naive_bayes
,
mlr_learners_classif.nnet
,
mlr_learners_classif.ranger
,
mlr_learners_classif.svm
,
mlr_learners_classif.xgboost
,
mlr_learners_regr.cv_glmnet
,
mlr_learners_regr.glmnet
,
mlr_learners_regr.kknn
,
mlr_learners_regr.km
,
mlr_learners_regr.lm
,
mlr_learners_regr.nnet
,
mlr_learners_regr.ranger
,
mlr_learners_regr.svm
,
mlr_learners_regr.xgboost
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifQDA
Examples
if (requireNamespace("MASS", quietly = TRUE)) {
learner = mlr3::lrn("classif.qda")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClassifQDA:classif.qda>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3learners, MASS
#> * Predict Types: [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: multiclass, twoclass, weights
#> [1] "method" "nu" "predict.method" "predict.prior"
#> [5] "prior"