predict
Predict responses using regression tree model
Description
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.
Input Arguments
tree
— Regression tree model
RegressionTree
model object | CompactRegressionTree
model object
Regression tree model, specified as a RegressionTree
model object trained with fitrtree
, or a CompactRegressionTree
model object created with
compact
.
X
— Predictor data
numeric matrix | table
Predictor data used to predict responses, specified as a numeric matrix or a table.
Each row of X
corresponds to one observation, and each
column corresponds to one variable.
For a numeric matrix:
The variables that make up the columns of
X
must have the same order as the predictor variables used to traintree
.If you train
tree
using a table (for example,Tbl
),X
can be a numeric matrix ifTbl
contains only numeric predictor variables. To treat numeric predictors inTbl
as categorical during training, specify categorical predictors using theCategoricalPredictors
name-value argument offitrtree
. IfTbl
contains heterogeneous predictor variables (for example, numeric and categorical data types) andX
is a numeric matrix,predict
issues an error.
For a table:
predict
does not support multicolumn variables or cell arrays other than cell arrays of character vectors.If you train
tree
using a table (for example,Tbl
), all predictor variables inX
must have the same variable names and data types as those used to traintree
(stored intree.PredictorNames
). However, the column order ofX
does not need to correspond to the column order ofTbl
.Tbl
andX
can contain additional variables, such as response variables and observation weights, butpredict
ignores them.If you train
tree
using a numeric matrix, the predictor names intree.PredictorNames
must be the same as the corresponding predictor variable names inX
. To specify predictor names during training, use thePredictorNames
name-value argument offitrtree
. All predictor variables inX
must be numeric vectors.X
can contain additional variables, such as response variables and observation weights, butpredict
ignores them.
subtrees
— Pruning level
0 (default) | vector of nonnegative integers | "all"
Pruning level, specified as 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(tree.PruneList)
.
0
indicates the full, unpruned tree, and
max(tree.PruneList)
indicates the completely pruned
tree (that is, just the root node).
If you specify "all"
, then
predict
operates on all subtrees (that is, the
entire pruning sequence). This specification is equivalent to using
0:max(tree.PruneList)
.
predict
prunes tree
to each
level specified by subtrees
, and then estimates the
corresponding output arguments. The size of subtrees
determines the size of some output arguments.
For the function to invoke subtrees
, the properties
PruneList
and PruneAlpha
of
tree
must be nonempty. In other words, grow
tree
by setting Prune="on"
when
you use fitrtree
, or by pruning tree
using prune
.
Data Types: single
| double
| char
| string
Output Arguments
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 then 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 tree
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 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(tree.PruneList)
.The
Subtrees
name-value 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:
The
predict
function does not support decision tree models trained with surrogate splits.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2011a
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