# predictorImportance

Estimates of predictor importance for classification tree

## Description

## Examples

### Estimate Predictor Importance Values

Load Fisher's iris data set.

`load fisheriris`

Grow a classification tree.

Mdl = fitctree(meas,species);

Compute predictor importance estimates for all predictor variables.

imp = predictorImportance(Mdl)

`imp = `*1×4*
0 0 0.0907 0.0682

The first two elements of `imp`

are zero. Therefore, the first two predictors do not enter into `Mdl`

calculations for classifying irises.

Estimates of predictor importance do not depend on the order of predictors if you use surrogate splits, but do depend on the order if you do not use surrogate splits.

Permute the order of the data columns in the previous example, grow another classification tree, and then compute predictor importance estimates.

measPerm = meas(:,[4 1 3 2]); MdlPerm = fitctree(measPerm,species); impPerm = predictorImportance(MdlPerm)

`impPerm = `*1×4*
0.1515 0 0.0074 0

The estimates of predictor importance are not a permutation of `imp`

.

### Surrogate Splits and Predictor Importance

Load Fisher's iris data set.

`load fisheriris`

Grow a classification tree. Specify usage of surrogate splits.

Mdl = fitctree(meas,species,'Surrogate','on');

Compute predictor importance estimates for all predictor variables.

imp = predictorImportance(Mdl)

`imp = `*1×4*
0.0791 0.0374 0.1530 0.1529

All predictors have some importance. The first two predictors are less important than the final two.

Permute the order of the data columns in the previous example, grow another classification tree specifying usage of surrogate splits, and then compute predictor importance estimates.

measPerm = meas(:,[4 1 3 2]); MdlPerm = fitctree(measPerm,species,'Surrogate','on'); impPerm = predictorImportance(MdlPerm)

`impPerm = `*1×4*
0.1529 0.0791 0.1530 0.0374

The estimates of predictor importance are a permutation of `imp`

.

### Unbiased Predictor Importance Estimates

Load the `census1994`

data set. Consider a model that predicts a person's salary category given their age, working class, education level, martial status, race, sex, capital gain and loss, and number of working hours per week.

load census1994 X = adultdata(:,{'age','workClass','education_num','marital_status','race',... 'sex','capital_gain','capital_loss','hours_per_week','salary'});

Display the number of categories represented in the categorical variables using `summary`

.

summary(X)

Variables: age: 32561x1 double Values: Min 17 Median 37 Max 90 workClass: 32561x1 categorical Values: Federal-gov 960 Local-gov 2093 Never-worked 7 Private 22696 Self-emp-inc 1116 Self-emp-not-inc 2541 State-gov 1298 Without-pay 14 NumMissing 1836 education_num: 32561x1 double Values: Min 1 Median 10 Max 16 marital_status: 32561x1 categorical Values: Divorced 4443 Married-AF-spouse 23 Married-civ-spouse 14976 Married-spouse-absent 418 Never-married 10683 Separated 1025 Widowed 993 race: 32561x1 categorical Values: Amer-Indian-Eskimo 311 Asian-Pac-Islander 1039 Black 3124 Other 271 White 27816 sex: 32561x1 categorical Values: Female 10771 Male 21790 capital_gain: 32561x1 double Values: Min 0 Median 0 Max 99999 capital_loss: 32561x1 double Values: Min 0 Median 0 Max 4356 hours_per_week: 32561x1 double Values: Min 1 Median 40 Max 99 salary: 32561x1 categorical Values: <=50K 24720 >50K 7841

Because there are few categories represented in the categorical variables compared to levels in the continuous variables, the standard CART, predictor-splitting algorithm prefers splitting a continuous predictor over the categorical variables.

Train a classification tree using the entire data set. To grow unbiased trees, specify usage of the curvature test for splitting predictors. Because there are missing observations in the data, specify usage of surrogate splits.

Mdl = fitctree(X,'salary','PredictorSelection','curvature',... 'Surrogate','on');

Estimate predictor importance values by summing changes in the risk due to splits on every predictor and dividing the sum by the number of branch nodes. Compare the estimates using a bar graph.

imp = predictorImportance(Mdl); figure; bar(imp); title('Predictor Importance Estimates'); ylabel('Estimates'); xlabel('Predictors'); h = gca; h.XTickLabel = Mdl.PredictorNames; h.XTickLabelRotation = 45; h.TickLabelInterpreter = 'none';

In this case, `capital_gain`

is the most important predictor, followed by `education_num`

.

## Input Arguments

`tree`

— Trained classification tree

`ClassificationTree`

model object | `CompactClassificationTree`

model object

Trained classification tree, specified as a `ClassificationTree`

or `CompactClassificationTree`

model object. That
is, `tree`

is a trained classification model returned by `fitctree`

or `compact`

.

## Output Arguments

`imp`

— Estimates of predictor importance

row vector

Estimates of predictor importance, returned as a row vector with the same number of
elements as the number of predictors (columns) in
`tree`

`.X`

. `0`

represents the
smallest possible importance.

## More About

### Predictor Importance

`predictorImportance`

computes importance measures of the predictors in a tree by
summing changes in the node risk due to splits on every predictor, and then dividing the sum
by the total number of branch nodes. The change in the node risk is the difference between
the risk for the parent node and the total risk for the two children. For example, if a tree
splits a parent node (for example, node 1) into two child nodes (for example, nodes 2 and
3), then `predictorImportance`

increases the importance of the split predictor by

(*R _{1}* –

*R*–

_{2}*R*)/

_{3}*N*

_{branch},

where *R _{i}* is the node risk of
node

*i*, and

*N*

_{branch}is the total number of branch nodes. A

*node risk*is defined as a node error or node impurity weighted by the node probability:

*R _{i}* =

*P*

_{i}*E*,

_{i}where *P _{i}* is the node
probability of node

*i*, and

*E*is either the node error (for a tree grown by minimizing the twoing criterion) or node impurity (for a tree grown by minimizing an impurity criterion, such as the Gini index or deviance) of node

_{i}*i*.

The estimates of predictor importance depend on whether you use surrogate splits for training.

If you use surrogate splits,

`predictorImportance`

sums the changes in the node risk over all splits at each branch node, including surrogate splits. If you do not use surrogate splits, then the function takes the sum over the best splits found at each branch node.Estimates of predictor importance do not depend on the order of predictors if you use surrogate splits, but do depend on the order if you do not use surrogate splits.

If you use surrogate splits,

`predictorImportance`

computes estimates before the tree is reduced by pruning (or merging leaves). If you do not use surrogate splits,`predictorImportance`

computes estimates after the tree is reduced by pruning. Therefore, pruning affects the predictor importance for a tree grown without surrogate splits, and does not affect the predictor importance for a tree grown with surrogate splits.

### Impurity and Node Error

A decision tree splits nodes based on either *impurity*
or *node error*.

Impurity means one of several things, depending on your choice of the
`SplitCriterion`

name-value
argument:

Gini's Diversity Index (

`gdi`

) — The Gini index of a node is$$1-{\displaystyle \sum _{i}{p}^{2}(i)},$$

where the sum is over the classes

*i*at the node, and*p*(*i*) is the observed fraction of classes with class*i*that reach the node. A node with just one class (a*pure*node) has Gini index`0`

; otherwise, the Gini index is positive. So the Gini index is a measure of node impurity.Deviance (

`"deviance"`

) — With*p*(*i*) defined the same as for the Gini index, the deviance of a node is$$-{\displaystyle \sum _{i}p(i){\mathrm{log}}_{2}p(i)}.$$

A pure node has deviance

`0`

; otherwise, the deviance is positive.Twoing rule (

`"twoing"`

) — Twoing is not a purity measure of a node, but is a different measure for deciding how to split a node. Let*L*(*i*) denote the fraction of members of class*i*in the left child node after a split, and*R*(*i*) denote the fraction of members of class*i*in the right child node after a split. Choose the split criterion to maximize$$P(L)P(R){\left({\displaystyle \sum _{i}\left|L(i)-R(i)\right|}\right)}^{2},$$

where

*P*(*L*) and*P*(*R*) are the fractions of observations that split to the left and right, respectively. If the expression is large, the split made each child node purer. Similarly, if the expression is small, the split made each child node similar to each other and, therefore, similar to the parent node. The split did not increase node purity.Node error — The node error is the fraction of misclassified classes at a node. If

*j*is the class with the largest number of training samples at a node, the node error is1 –

*p*(*j*).

## Extended Capabilities

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2011a**

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