# predictorImportance

Estimates of predictor importance for classification ensemble of decision trees

## Description

`[`

additionally returns a
`imp`

,`ma`

]
= predictorImportance(`ens`

)`P`

-by-`P`

matrix with predictive measures of
association `ma`

for `P`

predictors, when the
learners in `ens`

contain surrogate splits. For more information,
see Predictor Importance.

**Note**

You can compute predictor importance for ensembles of decision trees only.

## Examples

## Input Arguments

## Output Arguments

## More About

## Algorithms

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.

## Extended Capabilities

## Version History

**Introduced in R2011a**