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 trainedMdl
.If you trained
Mdl
using a table (for example,Tbl
), thenX
can be a numeric matrix ifTbl
contains all numeric predictor variables. To treat numeric predictors inTbl
as categorical during training, identify categorical predictors using theCategoricalPredictors
name-value pair argument offitrtree
. IfTbl
contains heterogeneous predictor variables (for example, numeric and categorical data types) andX
is a numeric matrix, thenpredict
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 inX
must have the same variable names and data types as those that trainedMdl
(stored inMdl.PredictorNames
). However, the column order ofX
does not need to correspond to the column order ofTbl
.Tbl
andX
can contain additional variables (response variables, observation weights, etc.), butpredict
ignores them.If you trained
Mdl
using a numeric matrix, then the predictor names inMdl.PredictorNames
and corresponding predictor variable names inX
must be the same. To specify predictor names during training, see thePredictorNames
name-value pair argument offitrtree
. All predictor variables inX
must be numeric vectors.X
can contain additional variables (response variables, observation weights, etc.), butpredict
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
andupdate
by using a coder configurer. Or, generate code only forpredict
by usingsaveLearnerForCoder
,loadLearnerForCoder
, andcodegen
.Code generation for
predict
andupdate
— Create a coder configurer by usinglearnerCoderConfigurer
and then generate code by usinggenerateCode
. 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 usingsaveLearnerForCoder
. Define an entry-point function that loads the saved model by usingloadLearnerForCoder
and calls thepredict
function. Then usecodegen
(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 theloadLearnerForCoder
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 bygenerateLearnerDataTypeFcn
, and use the structure as an input argument ofloadLearnerForCoder
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 inX
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 ofcodegen
(MATLAB Coder), wheren
ismax(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 becoder.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 agpuArray
or a table containinggpuArray
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 agpuArray
.You cannot specify the input argument
X
as a table containinggpuArray
elements.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
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