RegressionEnsemble Predict
Libraries:
Statistics and Machine Learning Toolbox /
Regression
Description
The RegressionEnsemble Predict block predicts responses using an ensemble
of decision trees (RegressionEnsemble
, RegressionBaggedEnsemble
, or CompactRegressionEnsemble
).
Import a trained regression object into the block by specifying the name of a workspace variable that contains the object. The input port x receives an observation (predictor data), and the output port yfit returns a predicted response for the observation.
Examples
Predict Responses Using RegressionEnsemble Predict Block
Train a regression ensemble model with optimal hyperparameters, and then use the RegressionEnsemble Predict block for response prediction.
Ports
Input
x — Predictor data
row vector | column vector
Predictor data, specified as a row or column vector of one observation.
The variables in x must have the same order as the predictor variables that trained the model specified by Select trained machine learning model.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Output
yfit — Predicted response
scalar
Predicted response, returned as a scalar.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Parameters
Main
Select trained machine learning model — Regression ensemble model
ensMdl
(default) | RegressionEnsemble
object | RegressionBaggedEnsemble
object | CompactRegressionEnsemble
object
Specify the name of a workspace variable that contains a RegressionEnsemble
object, RegressionBaggedEnsemble
object, or CompactRegressionEnsemble
object.
When you train the model by using fitrensemble
, the following restrictions apply:
The predictor data cannot include categorical predictors (
logical
,categorical
,char
,string
, orcell
). If you supply training data in a table, the predictors must be numeric (double
orsingle
). Also, you cannot use theCategoricalPredictors
name-value argument. To include categorical predictors in a model, preprocess them by usingdummyvar
before fitting the model.The value of the
ResponseTransform
name-value argument must be'none'
(default).You cannot use surrogate splits for tree weak learners, that is, the value of the
Surrogate
name-value argument must be'off'
(default) when you define tree weak learners by using thetemplateTree
function.
Programmatic Use
Block Parameter:
TrainedLearner |
Type: workspace variable |
Values:
RegressionEnsemble object |
RegressionBaggedEnsemble object |
CompactRegressionEnsemble object |
Default:
'ensMdl' |
Data Types
Fixed-Point Operational ParametersInteger rounding mode — Rounding mode for fixed-point operations
Floor
(default) | Ceiling
| Convergent
| Nearest
| Round
| Simplest
| Zero
Specify the rounding mode for fixed-point operations. For more information, see Rounding Modes (Fixed-Point Designer).
Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression into the mask field using a MATLAB® rounding function.
Programmatic Use
Block Parameter:
RndMeth |
Type: character vector |
Values:
"Ceiling" | "Convergent" | "Floor" | "Nearest" | "Round" | "Simplest" |
"Zero" |
Default:
"Floor" |
Saturate on integer overflow — Method of overflow action
off
(default) | on
Specify whether overflows saturate or wrap.
Action | Rationale | Impact on Overflows | Example |
---|---|---|---|
Select this check box
( | Your model has possible overflow, and you want explicit saturation protection in the generated code. | Overflows saturate to either the minimum or maximum value that the data type can represent. | The maximum value that the |
Clear this check box
( | You want to optimize the efficiency of your generated code. You want to avoid overspecifying how a block handles out-of-range signals. For more information, see Troubleshoot Signal Range Errors (Simulink). | Overflows wrap to the appropriate value that the data type can represent. | The maximum value that the |
Programmatic Use
Block Parameter:
SaturateOnIntegerOverflow |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type
off
(default) | on
Select this parameter to prevent the fixed-point tools from overriding the data type you specify for the block. For more information, see Use Lock Output Data Type Setting (Fixed-Point Designer).
Programmatic Use
Block Parameter:
LockScale |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Output data type — Data type of yfit output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the yfit output. The type can be inherited,
specified directly, or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that inherits a data type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter: OutDataTypeStr |
Type: character vector |
Values: "Inherit: auto" |
"double" |
"single" |
"half" |
"int8" |
"uint8" |
"int16" |
"uint16" |
"int32" |
"uint32" |
"int64" |
"uint64" |
"boolean" |
"fixdt(1,16,0)" |
"fixdt(1,16,2^0,0)" |
"<data type
expression>" |
Default: "Inherit: auto" |
Output data type Minimum — Minimum value of yfit output for range checking
[]
(default) | scalar
Specify the lower value of the yfit output range that Simulink® checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Output data type Minimum parameter does not saturate or clip the actual yfit signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
OutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Output data type Maximum — Maximum value of yfit output for range checking
[]
(default) | scalar
Specify the upper value of the yfit output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Output data type Maximum parameter does not saturate or clip the actual yfit signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
OutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Weak learner data type — Data type of weak learner outputs
Inherit:
auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type
expression>
Specify the data type for the outputs from weak learners. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that inherits a data type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter: WeakLearnerDataTypeStr |
Type: character vector |
Values: 'Inherit: auto' |
'double' |
'single' |
'half' |
'int8' |
'uint8' |
'int16' |
'uint16' |
'int32' |
'uint32' |
'int64' |
'uint64' |
'boolean' |
'fixdt(1,16,0)' |
'fixdt(1,16,2^0,0)' |
'<data type
expression>' |
Default: 'Inherit: auto' |
Weak learner data type Minimum — Minimum value of weak learner outputs for range checking
[]
(default) | scalar
Specify the lower value of the weak learner output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Weak learner data type Minimum parameter does not saturate or clip the actual weak learner output signals.
Programmatic Use
Block Parameter:
WeakLearnerOutMin |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Weak learner data type Maximum — Maximum value of weak learner outputs for range checking
[]
(default) | scalar
Specify the upper value of the weak learner output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Weak learner data type Maximum parameter does not saturate or clip the actual weak learner output signals.
Programmatic Use
Block Parameter:
WeakLearnerOutMax |
Type: character vector |
Values: '[]' |
scalar |
Default: '[]' |
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
Alternative Functionality
You can use a MATLAB Function block with the predict
object function of an ensemble of decision trees (RegressionEnsemble
, RegressionBaggedEnsemble
, or CompactRegressionEnsemble
). For an example, see Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the RegressionEnsemble Predict block in the
Statistics and Machine Learning Toolbox™ library or a MATLAB Function block with the predict
function, 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
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.
Version History
Introduced in R2021a
See Also
Blocks
- RegressionSVM Predict | RegressionTree Predict | RegressionNeuralNetwork Predict | RegressionGP Predict | ClassificationEnsemble Predict
Objects
Functions
MATLAB 命令
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