Estimates of predictor importance for classification ensemble of decision trees
imp = predictorImportance(ens)
[imp,ma]
= predictorImportance(ens)
computes estimates of predictor importance for imp
= predictorImportance(ens
)ens
by summing these
estimates over all weak learners in the ensemble. imp
has one element
for each input predictor in the data used to train this ensemble. A high value indicates
that this predictor is important for ens
.
[
returns a imp
,ma
]
= predictorImportance(ens
)P
byP
matrix with predictive measures
of association for P
predictors, when the learners in
ens
contain surrogate splits. See More About.

A classification ensemble of decision trees, created by 

A row vector with the same number of elements as the number of predictors
(columns) in 

A 
Element ma(i,j)
is the predictive measure of association averaged
over surrogate splits on predictor j
for which predictor
i
is the optimal split predictor. This average is computed by
summing positive values of the predictive measure of association over optimal splits on
predictor i
and surrogate splits on predictor j
and dividing by the total number of optimal splits on predictor i
,
including splits for which the predictive measure of association between predictors
i
and j
is negative.