RegressionTree Predict - Predict responses using regression tree model - Simulink (original) (raw)
Predict responses using regression tree model
Since R2021a
Libraries:
Statistics and Machine Learning Toolbox / Regression
Description
The RegressionTree Predict block predicts responses using a regression tree object (RegressionTree or CompactRegressionTree).
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
Ports
Input
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
Predicted response, returned as a scalar.
Data Types: single | double | half | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point
Parameters
Main
Specify the name of a workspace variable that contains a RegressionTree object orCompactRegressionTree object.
When you train the model by using fitrtree, 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 (doubleorsingle). Also, you cannot use theCategoricalPredictorsname-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; that is, the value of theSurrogate name-value argument must be
'off'(default).
Programmatic Use
| Block Parameter: TrainedLearner |
|---|
| Type: workspace variable |
| Values: RegressionTree object |CompactRegressionTree object |
| Default: 'treeMdl' |
Data Types
Fixed-Point Operational Parameters
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" |
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 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 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 asint8, 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 asSimulink.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)" | "" |
| Default: "Inherit: auto" |
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: '[]' |
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: '[]' |
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 |
More About
The data types of internal model parameters are synchronized to the data type of the output port, yfit.
Alternative Functionality
You can use a MATLAB Function block with the predict object function of a regression tree object (RegressionTree or CompactRegressionTree). For an example, seePredict Class Labels Using MATLAB Function Block.
When deciding whether to use the RegressionTree 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
predictfunction. - 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 | RegressionEnsemble Predict | RegressionNeuralNetwork Predict | ClassificationTree Predict