# predictorImportance

Estimates of predictor importance for regression 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.

## 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**