Description Slots Extends Methods Author(s) See Also

Object returned by all classifiers that can peform variable selection or compute variable importance. These are:

Random Forest, s.

`rfCMA`

,Componentwise Boosting, s.

`compBoostCMA`

,LASSO-logistic regression, s.

`LassoCMA`

,ElasticNet-logistic regression, s.

`ElasticNetCMA`

.
Objects of class `clvarseloutput`

extend both the class
`cloutuput`

and `varsel`

, s. below.

`learnind`

:Vector of indices that indicates which observations where used in the learning set.

`y`

:Actual (true) class labels of predicted observations.

`yhat`

:Predicted class labels by the classifier.

`prob`

:A

`numeric`

`matrix`

whose rows equals the number of predicted observations (length of`y`

/`yhat`

) and whose columns equal the number of different classes in the learning set. Rows add up to one. Entry`j,k`

of this matrix contains the probability for the`j`

-th predicted observation to belong to class`k`

. Can be a matrix of`NA`

s, if the classifier used does not provide any probabilities`method`

:Name of the classifer used.

`mode`

:`character`

, one of`"binary"`

(if the number of classes in the learning set is two) or`multiclass`

(if it is more than two).`varsel`

:`numeric`

vector of variable importance measures (for Random Forest) or absolute values of regression coefficients (for the other three methods mentionned above) (from which the majority will be zero).

Class `"cloutput"`

, directly.
Class `"varseloutput"`

, directly.

- show
Use

`show(cloutput-object)`

for brief information- ftable
Use

`ftable(cloutput-object)`

to obtain a confusion matrix/cross-tabulation of`y`

vs.`yhat`

, s.`ftable,cloutput-method`

.- plot
Use

`plot(cloutput-object)`

to generate a probability plot of the matrix`prob`

described above, s.`plot,cloutput-method`

- roc
Use

`roc(cloutput-object)`

to compute the empirical ROC curve and the Area Under the Curve (AUC) based on the predicted probabilities, s.`roc,cloutput-method`

Martin Slawski [email protected]

Anne-Laure Boulesteix [email protected]

`rfCMA`

, `compBoostCMA`

, `LassoCMA`

, `ElasticNetCMA`

chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.

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