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.