RegressionEnsemble Predict

Predict responses using ensemble of decision trees for regression

Since R2021a

• 

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.

Open Live Script

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, or cell). If you supply training data in a table, the predictors must be numeric (double or single). Also, you cannot use the CategoricalPredictors name-value argument. To include categorical predictors in a model, preprocess them by using dummyvar 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 the templateTree function.

Programmatic Use

Block Parameter: TrainedLearner

Type: workspace variable

Values: RegressionEnsemble object | RegressionBaggedEnsemble object | CompactRegressionEnsemble object

Default: 'ensMdl'

Data Types

Fixed-Point Operational Parameters

Integer 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 (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 (on).	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 int8 (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box selected, the block output saturates at 127. Similarly, the block output saturates at a minimum output value of –128.
Clear this check box (off).	You want to optimize 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 int8 (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box cleared, the software interprets the value causing the overflow as int8, which can produce an unintended result. For example, a block result of 130 (binary 1000 0010) expressed as int8 is –126.

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'

Data Type

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) | 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)' | 'fixdt(1,16,0)' | 'fixdt(1,16,2^0,0)' | '<data type expression>'

Default: 'Inherit: auto'

Output 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)).

• Automatic scaling of fixed-point data types.

• 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 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 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)).

• Automatic scaling of fixed-point data types.

• 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 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) | 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)' | 'fixdt(1,16,0)' | 'fixdt(1,16,2^0,0)' | '<data type expression>'

Default: 'Inherit: auto'

Weak learner 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)).

• Automatic scaling of fixed-point data types.

• 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 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 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)).

• Automatic scaling of fixed-point data types.

• 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 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	Boolean | double | fixed point | half | integer | single
Direct Feedthrough	yes
Multidimensional Signals	no
Variable-Size Signals	no
Zero-Crossing Detection	no

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