# predict

Predict responses using regression tree

## Description

predicts
response values with additional options specified by one or more `Yfit`

= predict(`Mdl`

,`X`

,`Name,Value`

)`Name,Value`

pair
arguments. For example, you can specify to prune `Mdl`

to
a particular level before predicting responses.

## Input Arguments

`Mdl`

— Trained regression tree

`RegressionTree`

model object | `CompactRegressionTree`

model object

Trained classification tree, specified as a `RegressionTree`

or `CompactRegressionTree`

model
object. That is, `Mdl`

is a trained classification
model returned by `fitrtree`

or `compact`

.

`X`

— Predictor data to be classified

numeric matrix | table

Predictor data to be classified, specified as a numeric matrix or table.

Each row of `X`

corresponds to one observation,
and each column corresponds to one variable.

For a numeric matrix:

The variables making up the columns of

`X`

must have the same order as the predictor variables that trained`Mdl`

.If you trained

`Mdl`

using a table (for example,`Tbl`

), then`X`

can be a numeric matrix if`Tbl`

contains all numeric predictor variables. To treat numeric predictors in`Tbl`

as categorical during training, identify categorical predictors using the`CategoricalPredictors`

name-value pair argument of`fitrtree`

. If`Tbl`

contains heterogeneous predictor variables (for example, numeric and categorical data types) and`X`

is a numeric matrix, then`predict`

throws an error.

For a table:

`predict`

does not support multicolumn variables or cell arrays other than cell arrays of character vectors.If you trained

`Mdl`

using a table (for example,`Tbl`

), then all predictor variables in`X`

must have the same variable names and data types as those that trained`Mdl`

(stored in`Mdl.PredictorNames`

). However, the column order of`X`

does not need to correspond to the column order of`Tbl`

.`Tbl`

and`X`

can contain additional variables (response variables, observation weights, etc.), but`predict`

ignores them.If you trained

`Mdl`

using a numeric matrix, then the predictor names in`Mdl.PredictorNames`

and corresponding predictor variable names in`X`

must be the same. To specify predictor names during training, see the`PredictorNames`

name-value pair argument of`fitrtree`

. All predictor variables in`X`

must be numeric vectors.`X`

can contain additional variables (response variables, observation weights, etc.), but`predict`

ignores them.

**Data Types: **`table`

| `double`

| `single`

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

`Subtrees`

— Pruning level

0 (default) | vector of nonnegative integers | `'all'`

Pruning level, specified as the comma-separated pair consisting
of `'Subtrees'`

and a vector of nonnegative integers
in ascending order or `'all'`

.

If you specify a vector, then all elements must be at least `0`

and
at most `max(Mdl.PruneList)`

. `0`

indicates
the full, unpruned tree and `max(Mdl.PruneList)`

indicates
the completely pruned tree (i.e., just the root node).

If you specify `'all'`

, then `predict`

operates
on all subtrees (i.e., the entire pruning sequence). This specification
is equivalent to using `0:max(Mdl.PruneList)`

.

`predict`

prunes `Mdl`

to
each level indicated in `Subtrees`

, and then estimates
the corresponding output arguments. The size of `Subtrees`

determines
the size of some output arguments.

To invoke `Subtrees`

, the properties `PruneList`

and `PruneAlpha`

of `Mdl`

must
be nonempty. In other words, grow `Mdl`

by setting `'Prune','on'`

,
or by pruning `Mdl`

using `prune`

.

**Example: **`'Subtrees','all'`

**Data Types: **`single`

| `double`

| `char`

| `string`

## Output Arguments

## Examples

### Predict a Response Using a Regression Tree

Load the `carsmall`

data set. Consider `Displacement`

, `Horsepower`

, and `Weight`

as predictors of the response `MPG`

.

```
load carsmall
X = [Displacement Horsepower Weight];
```

Grow a regression tree using the entire data set.

Mdl = fitrtree(X,MPG);

Predict the MPG for a car with 200 cubic inch engine displacement, 150 horsepower, and that weighs 3000 lbs.

X0 = [200 150 3000]; MPG0 = predict(Mdl,X0)

MPG0 = 21.9375

The regression tree predicts the car's efficiency to be 21.94 mpg.

## Alternative Functionality

### Simulink Block

To integrate the prediction of a regression tree model into Simulink^{®}, you can use the RegressionTree
Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^{®} Function block with the `predict`

function. For
examples, see Predict Responses Using RegressionTree Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

If you use the Statistics and Machine Learning Toolbox library block, you can use the

**Fixed-Point Tool (Fixed-Point Designer)**to convert a floating-point model to fixed point.Support for variable-size arrays must be enabled for a MATLAB Function block with the

`predict`

function.If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

## Extended Capabilities

### Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.

For more information, see Tall Arrays.

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

You can generate C/C++ code for both

`predict`

and`update`

by using a coder configurer. Or, generate code only for`predict`

by using`saveLearnerForCoder`

,`loadLearnerForCoder`

, and`codegen`

.Code generation for

`predict`

and`update`

— Create a coder configurer by using`learnerCoderConfigurer`

and then generate code by using`generateCode`

. Then you can update model parameters in the generated code without having to regenerate the code.Code generation for

`predict`

— Save a trained model by using`saveLearnerForCoder`

. Define an entry-point function that loads the saved model by using`loadLearnerForCoder`

and calls the`predict`

function. Then use`codegen`

(MATLAB Coder) to generate code for the entry-point function.

To generate single-precision C/C++ code for

`predict`

, specify the name-value argument`"DataType","single"`

when you call the`loadLearnerForCoder`

function.You can also generate fixed-point C/C++ code for

`predict`

. Fixed-point code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using the data type function generated by`generateLearnerDataTypeFcn`

, and use the structure as an input argument of`loadLearnerForCoder`

in an entry-point function. Generating fixed-point C/C++ code requires MATLAB Coder™ and Fixed-Point Designer™.This table contains notes about the arguments of

`predict`

. Arguments not included in this table are fully supported.Argument Notes and Limitations `Mdl`

For the usage notes and limitations of the model object, see Code Generation of the

`CompactRegressionTree`

object.`X`

For general code generation,

`X`

must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both.In the coder configurer workflow,

`X`

must be a single-precision or double-precision matrix.For fixed-point code generation,

`X`

must be a fixed-point matrix.The number of rows, or observations, in

`X`

can be a variable size, but the number of columns in`X`

must be fixed.If you want to specify

`X`

as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following:Accept data as arrays.

Create a table from the data input arguments and specify the variable names in the table.

Pass the table to

`predict`

.

For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).

`Subtrees`

Names in name-value pair arguments must be compile-time constants. For example, to allow user-defined pruning levels in the generated code, include

`{coder.Constant('Subtrees'),coder.typeof(0,[1,n],[0,1])}`

in the`-args`

value of`codegen`

(MATLAB Coder), where`n`

is`max(Mdl.PruneList)`

.The

`'Subtrees'`

name-value pair argument is not supported in the coder configurer workflow.For fixed-point code generation, the

`'Subtrees'`

value must be`coder.Constant('all')`

or have an integer data type.

For more information, see Introduction to Code Generation.

### GPU Arrays

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

Usage notes and limitations:

`predict`

executes on a GPU in these cases:The input argument

`X`

is a`gpuArray`

or a table containing`gpuArray`

predictor variables.The input argument

`mdl`

was fitted with GPU array input arguments.

If the tree model was trained with surrogate splits, these limitations apply:

You cannot specify the input argument

`X`

as a`gpuArray`

.You cannot specify the input argument

`X`

as a table containing`gpuArray`

elements.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2011a**

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