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RegressionEnsemble Predict

Predict responses using ensemble of decision trees for regression

  • Library:
  • 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.

Ports

Input

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Predictor data, specified as a column vector or row vector of one observation.

Dependencies

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Output

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Predicted response, returned as a scalar.

Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Parameters

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Main

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 the categorical predictors 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

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 using a MATLAB® rounding function into the mask field.

Programmatic Use

Block Parameter: RndMeth
Type: character vector
Values: 'Ceiling' | 'Convergent' | 'Floor' | 'Nearest' | 'Round' | 'Simplest' | 'Zero'
Default: 'Floor'

Specify whether overflows saturate or wrap.

ActionRationaleImpact on OverflowsExample

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 overflow causing value 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'

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

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 Signal Data Types (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'

Lower value of the yfit output range that Simulink® checks.

Simulink uses the minimum value to perform:

Note

The Output minimum parameter does not saturate or clip the actual yfit signal. Use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: OutMin
Type: character vector
Values: '[]' | scalar
Default: '[]'

Upper value of the yfit output range that Simulink checks.

Simulink uses the minimum value to perform:

Note

The Output maximum parameter does not saturate or clip the actual yfit signal. Use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: OutMax
Type: character vector
Values: '[]' | scalar
Default: '[]'

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 Signal Data Types (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'

Lower value of the weak learner output range that Simulink checks.

Simulink uses the minimum value to perform:

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: '[]'

Upper value of the weak learner output range that Simulink checks.

Simulink uses the maximum value to perform:

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™.

Introduced in R2021a