anova.test |
|
ANOVA Test for binary and multiclass classification tasks |
X |
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X |
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auc |
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AUC filter for binary classification tasks |
X |
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|
X |
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carscore |
care |
CAR scores |
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X |
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|
X |
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FSelector_chi.squared |
FSelector |
Chi-squared statistic of independence between feature and target |
X |
X |
|
X |
X |
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FSelector_gain.ratio |
FSelector |
Entropy-based gain ratio between feature and target |
X |
X |
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X |
X |
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FSelector_information.gain |
FSelector |
Entropy-based information gain between feature and target |
X |
X |
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X |
X |
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FSelector_oneR |
FSelector |
oneR association rule |
X |
X |
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X |
X |
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FSelector_relief |
FSelector |
RELIEF algorithm |
X |
X |
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X |
X |
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FSelector_symmetrical.uncertainty |
FSelector |
Entropy-based symmetrical uncertainty between feature and target |
X |
X |
|
X |
X |
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FSelectorRcpp_gain.ratio |
FSelectorRcpp |
Entropy-based Filters: Algorithms that find ranks of importance of discrete attributes, basing on their entropy with a continous class attribute |
X |
X |
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X |
X |
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FSelectorRcpp_information.gain |
FSelectorRcpp |
Entropy-based Filters: Algorithms that find ranks of importance of discrete attributes, basing on their entropy with a continous class attribute |
X |
X |
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X |
X |
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FSelectorRcpp_relief |
FSelectorRcpp |
RELIEF algorithm |
X |
X |
|
X |
X |
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FSelectorRcpp_symmetrical.uncertainty |
FSelectorRcpp |
Entropy-based Filters: Algorithms that find ranks of importance of discrete attributes, basing on their entropy with a continous class attribute |
X |
X |
|
X |
X |
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kruskal.test |
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Kruskal Test for binary and multiclass classification tasks |
X |
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X |
X |
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linear.correlation |
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Pearson correlation between feature and target |
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X |
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X |
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mrmr |
mRMRe |
Minimum redundancy, maximum relevance filter |
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X |
X |
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X |
X |
party_cforest.importance |
party |
Permutation importance of random forest fitted in package ‘party’ |
X |
X |
X |
X |
X |
X |
permutation.importance |
|
Aggregated difference between feature permuted and unpermuted predictions |
X |
X |
X |
X |
X |
X |
praznik_CMIM |
praznik |
Minimal conditional mutual information maximisation filter |
X |
X |
|
X |
X |
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praznik_DISR |
praznik |
Double input symmetrical relevance filter |
X |
X |
|
X |
X |
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praznik_JMI |
praznik |
Joint mutual information filter |
X |
X |
|
X |
X |
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praznik_JMIM |
praznik |
Minimal joint mutual information maximisation filter |
X |
X |
|
X |
X |
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praznik_MIM |
praznik |
conditional mutual information based feature selection filters |
X |
X |
|
X |
X |
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praznik_MRMR |
praznik |
Minimum redundancy maximal relevancy filter |
X |
X |
|
X |
X |
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praznik_NJMIM |
praznik |
Minimal normalised joint mutual information maximisation filter |
X |
X |
|
X |
X |
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randomForest_importance |
randomForest |
Importance based on OOB-accuracy or node inpurity of random forest fitted in package ‘randomForest’. |
X |
X |
|
X |
X |
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ranger_impurity |
ranger |
Variable importance based on ranger impurity importance |
X |
X |
|
X |
X |
X |
ranger_permutation |
ranger |
Variable importance based on ranger permutation importance |
X |
X |
X |
X |
X |
X |
rank.correlation |
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Spearman’s correlation between feature and target |
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X |
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|
X |
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univariate.model.score |
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Resamples an mlr learner for each input feature individually. The resampling performance is used as filter score, with rpart as default learner. |
X |
X |
X |
X |
X |
X |
variance |
|
A simple variance filter |
X |
X |
X |
|
X |
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