ClassificationKernel Predict

Classify observations using Gaussian kernel classifier for binary classification

Since R2024b

Libraries:
Statistics and Machine Learning Toolbox / Classification

Description

The ClassificationKernel Predict block classifies observations using a kernel classification object (ClassificationKernel) for binary classification with random feature expansion.

Import a trained kernel classification 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 label returns a predicted class label for the observation. The optional output port score returns the predicted class scores or posterior probabilities.

Examples

New

Predict Class Labels Using ClassificationKernel Predict Block

Train a Gaussian kernel classification model, and then use the ClassificationKernel Predict block for label prediction.

Since R2024b
Open Live Script

Ports

Input

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x — Predictor data
row vector | column vector

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

The variables in x must have the same order as the predictor variables that trained the kernel classification model specified by Select trained machine learning model.

If you set Standardize=true in fitckernel when training the model, then the ClassificationKernel Predict block standardizes the values of x using the means and standard deviations in the Mu and Sigma properties (respectively) of the model.

Output

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label — Predicted class label
scalar

Predicted class label, returned as a scalar corresponding to the class that yields the highest score. For more details, see the Label argument of the predict object function.

score — Predicted class scores or posterior probabilities
1-by-2 vector

Predicted class scores or posterior probabilities, returned as a 1-by-2 vector.

The first and second element of score correspond to the classification scores of the negative class (kernelMdl.ClassNames(1)) and the positive class (kernelMdl.ClassNames(2)), respectively, where kernelMdl is the kernel model specified by Select trained machine learning model. You can use the ClassNames property of kernelMdl to check the negative and positive class names. If kernelMdl.Learner is 'logistic', then the classification scores are posterior probabilities.

Dependencies

To enable this port, select the check box for Add output port for predicted class scores on the Main tab of the Block Parameters dialog box.

Parameters

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To edit block parameters interactively, use the Property Inspector. From the Simulink^® Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.

Main

Select trained machine learning model — Kernel classification model
`kernelMdl` (default) | `ClassificationKernel` object

Specify the name of a workspace variable that contains a ClassificationKernel object.

When you train the kernel classification model by using fitckernel, 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 ScoreTransform name-value argument cannot be 'invlogit' or an anonymous function.

Programmatic Use

Block Parameter: TrainedLearner

Type: character vector or string

Values: ClassificationKernel object name

Default: "kernelMdl"

Add output port for predicted class scores — Add second output port for predicted class scores
`off` (default) | `on`

Select the check box to include the output port score in the ClassificationKernel Predict block.

Programmatic Use

Block Parameter: ShowOutputScore

Type: character vector or string

Values: "off"|"on"

Default: "off"

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 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

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 as `int8`, which can produce an unintended result. For example, a block result of 130 (binary 1000 0010) expressed as `int8` is –126.

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 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

Label data type — Data type of label output
`Inherit: Inherit via back propagation` | `Inherit: auto` | `double` | `single` | `half` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `uint32` | `int64` | `uint64` | `boolean` | `fixdt(1,16,0)` | `fixdt(1,16,2^0,0)` | `Enum: <class name>` | `<data type expression>`

Specify the data type for the label output. The type can be inherited, specified as an enumerated data type, or expressed as a data type object such as Simulink.NumericType.

The supported data types depend on the labels used in the model specified by Select trained machine learning model.

If the model uses numeric or logical labels, the supported data types are Inherit: Inherit via back propagation (default), double, single, half, int8, uint8, int16, uint16, int32, uint32, int64, uint64, boolean, fixed point, and a data type object.
If the model uses nonnumeric labels, the supported data types are Inherit: auto (default), Enum: <class name>, and a data type object.

When you select an inherited option, the software behaves as follows:

Inherit: Inherit via back propagation (default for numeric and logical labels) — Simulink automatically determines the Label data type of the block during data type propagation (see Data Type Propagation (Simulink)). In this case, the block uses the data type of a downstream block or signal object.
Inherit: auto (default for nonnumeric labels) — The block uses an autodefined enumerated data type variable. For example, suppose the workspace variable name specified by Select trained machine learning model is myMdl, and the class labels are class 1 and class 2. Then, the corresponding label values are myMdl_enumLabels.class_1 and myMdl_enumLabels.class_2. The block converts the class labels to valid MATLAB identifiers by using the matlab.lang.makeValidName function.

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: LabelDataTypeStr

Type: character vector

Values:

"Inherit: Inherit via back
                        propagation"

"<data type
                        expression>"

Default:

"Inherit: Inherit via
                        back propagation"

(for numeric and logical labels) | "Inherit: auto" (for nonnumeric labels)

Label data type Minimum — Minimum value of label output for range checking
`[]` (default) | scalar

Specify the lower value of the label 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 Label data type Minimum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.

Dependencies

You can specify this parameter only if the model specified by Select trained machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Label data type Maximum — Maximum value of label output for range checking
`[]` (default) | scalar

Specify the upper value of the label 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 Label data type Maximum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.

Dependencies

You can specify this parameter only if the model specified by Select trained machine learning model uses numeric labels.

Programmatic Use

Block Parameter: LabelOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Score data type — Data type of score 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 score 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).

Programmatic Use

Block Parameter: ScoreDataTypeStr

Type: character vector

Default:

"Inherit:
                    auto"

Score data type Minimum — Minimum value of score output for range checking
`[]` (default) | scalar

Specify the lower value of the score 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 Score data type Minimum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: ScoreOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Score data type Maximum — Maximum value of score output for range checking
`[]` (default) | scalar

Specify the upper value of the score 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 Score data type Maximum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: ScoreOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Raw score data type — Untransformed score data type
`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 internal untransformed scores. 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).

Dependencies

You can specify this parameter only if the model specified by Select trained machine learning model uses a score transformation other than "none" (default, same as "identity").

If the model uses no score transformations ("none" or "identity"), then you can specify the score data type by using Score data type.
If the model uses a score transformation other than "none" or "identity", then you can specify the data type of untransformed raw scores by using this parameter. To specify the data type of transformed scores, use Score data type.

You can change the score transformation option by specifying the ScoreTransform name-value argument during training, or by modifying the ScoreTransform property after training.

Programmatic Use

Block Parameter: RawScoreDataTypeStr

Type: character vector

Default: "Inherit: auto"

Raw score data type Minimum — Minimum untransformed score for range checking
`[]` (default) | scalar

Specify the lower value of the untransformed score 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 Raw score data type Minimum parameter does not saturate or clip the actual untransformed score signal.

Programmatic Use

Block Parameter: RawScoreOutMin

Type: character vector

Values: "[]" | scalar

Default: "[]"

Raw score data type Maximum — Maximum untransformed score for range checking
`[]` (default) | scalar

Specify the upper value of the untransformed score 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 Raw score data type Maximum parameter does not saturate or clip the actual untransformed score signal.

Programmatic Use

Block Parameter: RawScoreOutMax

Type: character vector

Values: "[]" | scalar

Default: "[]"

Additional Data Types

Kernel data type — Kernel computation data type
`Inherit: Inherit via internal rule` (default) | `double` | `single` | `half` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `int64` | `uint64` | `uint32` | `boolean` | `fixdt(1,16,0)` | `fixdt(1,16,2^0,0)` | `<data type expression>`

Specify the data type of the parameters for Gaussian kernel approximation computation. The type can be specified directly or expressed as a data type object such as Simulink.NumericType.

When you select Inherit: Inherit via internal rule, the block uses an internal rule to determine the kernel data type. The internal rule chooses a data type that optimizes numerical accuracy, performance, and generated code size, while taking into account the properties of the embedded target hardware. The software cannot always optimize efficiency and numerical accuracy at the same time.

For more information about data types, see Control Data Types of Signals (Simulink).

Programmatic Use

Block Parameter: KernelDataTypeStr

Type: character vector

'<data type
                        expression>'

Default:

'Inherit: Inherit via
                        internal rule'

Kernel data type Minimum — Minimum kernel computation value for range checking
`[]` (default) | scalar

Specify the lower value of the kernel computation internal variable 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 Kernel data type Minimum parameter does not saturate or clip the actual kernel computation value.

Programmatic Use

Block Parameter: KernelOutMin

Type: character vector

Values: '[]' | scalar

Default: '[]'

Kernel data type Maximum — Maximum kernel computation value for range checking
`[]` (default) | scalar

Specify the upper value of the kernel computation internal variable 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 Kernel data type Maximum parameter does not saturate or clip the actual kernel computation value.

Programmatic Use

Block Parameter: KernelOutMax

Type: character vector

Values: '[]' | scalar

Default: '[]'

Block Characteristics

Data Types	`Boolean` \| `double` \| `enumerated` \| `fixed point` \| `half` \| `integer` \| `single`
Direct Feedthrough	`yes`
Multidimensional Signals	`no`
Variable-Size Signals	`no`
Zero-Crossing Detection	`no`

More About

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Classification Score

For kernel classification models, the raw classification score for classifying the observation x, a row vector, into the positive class is defined by

$f (x) = T (x) β + b .$

$T (\cdot)$ is a transformation of an observation for feature expansion.
β is the estimated column vector of coefficients.
b is the estimated scalar bias.

The raw classification score for classifying x into the negative class is −f(x). The software classifies observations into the class that yields a positive score.

If the kernel classification model consists of logistic regression learners, then the software applies the 'logit' score transformation to the raw classification scores (see ScoreTransform).

Tips

To predict labels using a trained ClassificationPartitionedKernel model Mdl with cross-validated folds, access the internal ClassificationKernel model using Mdl.Trained{i}, where i is the index of the internal model.

Alternative Functionality

You can use a MATLAB Function (Simulink) block with the predict object function of a kernel classification object (ClassificationKernel). For an example of using a MATLAB Function block, see Predict Class Labels Using MATLAB Function Block.

When deciding whether to use the ClassificationKernel 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 R2024b

ClassificationKernel Predict

Description

Examples

Predict Class Labels Using ClassificationKernel Predict Block

Ports

Input

x — Predictor data row vector | column vector

Output

label — Predicted class label scalar

score — Predicted class scores or posterior probabilities 1-by-2 vector

Dependencies

Parameters

Main

Select trained machine learning model — Kernel classification model kernelMdl (default) | ClassificationKernel object

Programmatic Use

Add output port for predicted class scores — Add second output port for predicted class scores off (default) | on

Programmatic Use

Data Types

Integer rounding mode — Rounding mode for fixed-point operations Floor (default) | Ceiling | Convergent | Nearest | Round | Simplest | Zero

Programmatic Use

Saturate on integer overflow — Method of overflow action off (default) | on

Programmatic Use

Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type off (default) | on

Programmatic Use

Label data type — Data type of label output Inherit: Inherit via back propagation | Inherit: auto | double | single | half | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | Enum: <class name> | <data type expression>

Programmatic Use

Label data type Minimum — Minimum value of label output for range checking [] (default) | scalar

Dependencies

Programmatic Use

Label data type Maximum — Maximum value of label output for range checking [] (default) | scalar

Dependencies

Programmatic Use

Score data type — Data type of score 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>

Programmatic Use

Score data type Minimum — Minimum value of score output for range checking [] (default) | scalar

Programmatic Use

Score data type Maximum — Maximum value of score output for range checking [] (default) | scalar

Programmatic Use

Raw score data type — Untransformed score data type 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>

Dependencies

Programmatic Use

Raw score data type Minimum — Minimum untransformed score for range checking [] (default) | scalar

Programmatic Use

Raw score data type Maximum — Maximum untransformed score for range checking [] (default) | scalar

Programmatic Use

Kernel data type — Kernel computation data type Inherit: Inherit via internal rule (default) | double | single | half | int8 | uint8 | int16 | uint16 | int32 | int64 | uint64 | uint32 | boolean | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Programmatic Use

Kernel data type Minimum — Minimum kernel computation value for range checking [] (default) | scalar

Programmatic Use

Kernel data type Maximum — Maximum kernel computation value for range checking [] (default) | scalar

Programmatic Use

Block Characteristics

More About

Classification Score

Tips

Alternative Functionality

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

See Also

Blocks

Objects

Functions

Topics

x — Predictor data
row vector | column vector

label — Predicted class label
scalar

score — Predicted class scores or posterior probabilities
1-by-2 vector

Select trained machine learning model — Kernel classification model
`kernelMdl` (default) | `ClassificationKernel` object

Add output port for predicted class scores — Add second output port for predicted class scores
`off` (default) | `on`

Integer rounding mode — Rounding mode for fixed-point operations
`Floor` (default) | `Ceiling` | `Convergent` | `Nearest` | `Round` | `Simplest` | `Zero`

Saturate on integer overflow — Method of overflow action
`off` (default) | `on`

Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type
`off` (default) | `on`

Label data type Minimum — Minimum value of label output for range checking
`[]` (default) | scalar

Label data type Maximum — Maximum value of label output for range checking
`[]` (default) | scalar

Score data type — Data type of score 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>`

Score data type Minimum — Minimum value of score output for range checking
`[]` (default) | scalar

Score data type Maximum — Maximum value of score output for range checking
`[]` (default) | scalar

Raw score data type — Untransformed score data type
`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>`

Raw score data type Minimum — Minimum untransformed score for range checking
`[]` (default) | scalar

Raw score data type Maximum — Maximum untransformed score for range checking
`[]` (default) | scalar

Kernel data type — Kernel computation data type
`Inherit: Inherit via internal rule` (default) | `double` | `single` | `half` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `int64` | `uint64` | `uint32` | `boolean` | `fixdt(1,16,0)` | `fixdt(1,16,2^0,0)` | `<data type expression>`

Kernel data type Minimum — Minimum kernel computation value for range checking
`[]` (default) | scalar

Kernel data type Maximum — Maximum kernel computation value for range checking
`[]` (default) | scalar

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™.