ClassificationSVM Predict

Classify observations using support vector machine (SVM) classifier for one-class and binary classification

Library:
Statistics and Machine Learning Toolbox / Classification

Description

The ClassificationSVM Predict block classifies observations using an SVM classification object ClassificationSVM and its object function predict for one-class and two-class (binary) classification.

Import a trained SVM classification object into the block by specifying the name of a workspace variable that contains a ClassificationSVM object. The input port X receives an observation (predictor data), and the output port Label returns a predicted class label for the observation. You can add an optional output port Score that returns predicted class scores or posterior probabilities.

Ports

Input

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`X` — Predictor data
row vector | column vector

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

Dependencies

The variables in X must have the same order as the predictor variables that trained the SVM model specified by Select trained machine learning model.
If you set 'Standardize',true in fitcsvm when training the SVM model, then the ClassificationSVM Predict block standardizes the values of X using the means and standard deviations in the Mu and Sigma properties (respectively) of the SVM model.

Output

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`Label` — Predicted class label
scalar

Predicted class label, returned as a scalar.

Dependencies

For one-class learning, Label is the value representing the positive class.
For two-class learning, Label is the class yielding the largest score or the largest posterior probability.

`Score` — Predicted class scores or posterior probabilities
scalar | 1-by-2 vector

Predicted class scores or posterior probabilities, returned as a scalar for one-class learning or a 1-by-2 vector for two-class learning.

For one-class learning, Score is the classification score of the positive class. You cannot obtain posterior probabilities for one-class learning.
For two-class learning, Score is a 1-by-2 vector.
- The first and second element of Score correspond to the classification scores of the negative class (svmMdl.ClassNames(1)) and the positive class (svmMdl.ClassNames(2)), respectively, where svmMdl is the SVM model specified by Select trained machine learning model. You can use the ClassNames property of svmMdl to check the negative and positive class names.
- If you fit the optimal score-to-posterior-probability transformation function using fitPosterior or fitSVMPosterior, then Score contains class posterior probabilities. Otherwise, Score contains class scores.

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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Main

`Select trained machine learning model` — SVM classification model
`svmMdl` (default) | `ClassificationSVM` object | `CompactClassificationSVM` object

Specify the name of a workspace variable that contains a ClassificationSVM object or CompactClassificationSVM object.

When you train the SVM model by using fitcsvm, 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 pair argument. To include categorical predictors in an SVM model, preprocess the categorical predictors by using dummyvar before fitting the SVM model.
The value of the 'ScoreTransform' name-value pair argument cannot be 'invlogit' or an anonymous function. For a block that predicts posterior probabilities given new observations, pass a trained SVM model to fitPosterior or fitSVMPosterior.
The value of the 'KernelFunction' name-value pair argument must be 'gaussian', 'linear', or 'polynomial'.

Programmatic Use

Block Parameter: TrainedLearner

Type: workspace variable

Values: ClassificationSVM object | CompactClassificationSVM object

Default: svmMdl

`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 second output port Score in the ClassificationSVM Predict block.

Programmatic Use

Block Parameter: ShowOutputScore

Type: character vector

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 (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'

`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 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.

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'

`Lock output data type setting against changes by the fixed-point tools` — Prevent 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` (default) | `Inherit: Inherit from 'Constant value'` | `double` | `single` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `uint32` | `int64` | `uint64` | `boolean` | `fixdt(1,16)` | `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 directly, or expressed as a data type object such as Simulink.NumericType.

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

Inherit: Inherit via back propagation — 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: Inherit from 'Constant value' — The ClassificationSVM predict block uses a Constant block under the mask. The data type of the Constant block depends on the observed class labels that trained the SVM model specified by Select trained machine learning model.
- If the observed class labels are numeric, then the Constant block data type is double.
- If the observed class labels are logical, then the Constant block data type is boolean.
- If the observed class labels are not numeric or logical, then the Constant block uses an enumerated data type variable enumLabels. For example, if the class labels are class 1 and class 2, then the corresponding Label values are enumLabels.class_1 and 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 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: LabelDataTypeStr

Type: character vector

Values:

'Inherit:
										Inherit via back propagation'

Inherit:
										Inherit from 'Constant value'

Enum:
										<class name>

'<data type
										expression>'

Default:

'Inherit:
										Inherit via back propagation'

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

Lower value of the Label output range that Simulink^® checks.

Simulink uses the minimum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Label minimum parameter does not saturate or clip the actual Label output signal. Use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: LabelOutMin

Type: character vector

Values:

'[
										]'

| scalar

Default:

'[
										]'

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

Upper value of the Label output range that Simulink checks.

Simulink uses the maximum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Label maximum parameter does not saturate or clip the actual Label output signal. Use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: LabelOutMax

Type: character vector

Values:

'[
										]'

| scalar

Default:

'[
										]'

`Score data type` — Data type of Score output
`double` (default) | `Inherit: auto` | `single` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `uint32` | `int64` | `uint64` | `fixdt(1,16)` | `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 Signal Data Types (Simulink).

Programmatic Use

Block Parameter: ScoreDataTypeStr

Type: character vector

Values:

'Inherit:
										auto'

'<data
										type expression>'

Default: 'double'

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

Lower value of the Score output range that Simulink checks.

Simulink uses the minimum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Score minimum parameter does not saturate or clip the actual Score signal. Use the Saturation (Simulink) block instead.

Programmatic Use

Block Parameter: ScoreOutMin

Type: character vector

Values:

'[
										]'

| scalar

Default:

'[
										]'

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

Upper value of the Score output range that Simulink checks.

Simulink uses the maximum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Score maximum parameter does not saturate or clip the actual Score signal. 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
`double` (default) | `Inherit: auto` | `single` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `uint32` | `int64` | `uint64` | `fixdt(1,16)` | `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 Signal Data Types (Simulink).

Programmatic Use

Block Parameter: RawScoreDataTypeStr

Type: character vector

Values:

'Inherit:
										auto'

'<data
										type expression>'

Default: 'double'

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

Lower value of the untransformed score range that Simulink checks.

Simulink uses the minimum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Raw score 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 maximum` — Maximum untransformed score for range checking
`[]` (default) | scalar

Upper value of the untransformed score range that Simulink checks.

Simulink uses the maximum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Raw score maximum parameter does not saturate or clip the actual untransformed score signal.

Programmatic Use

Block Parameter: RawScoreOutMax

Type: character vector

Values:

'[
										]'

| scalar

Default:

'[
										]'

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

Specify the data type of a parameter for kernel computation.

The Kernel data type paramter specifies the data type of a different parameter depending on the type of kernel function of the specified SVM model. You specify the 'KernelFunction' name-value pair argument when training the SVM model.

`'KernelFunction'` value	Data Type
`'gaussian'` or `'rbf'`	Kernel data type specifies the data type of the squared distance $D^{2} = {‖ x - s ‖}^{2}$ for the Gaussian kernel $G (x, s) = \exp (- D^{2})$ , where x is the predictor data for an observation and s is a support vector.
`'linear'`	Kernel data type specifies the data type for the output of the linear kernel function $G (x, s) = x s'$ , where x is the predictor data for an observation and s is a support vector.
`'polynomial'`	Kernel data type specifies the data type for the output of the polynomial kernel function $G (x, s) = {(1 + x s')}^{p}$ , where x is the predictor data for an observation, s is a support vector, and p is a polynomial kernel function order.

The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType.

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

Inherit: Inherit via internal rule — The block uses an internal rule to determine its 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 simultaneously optimize efficiency and numerical accuracy in all situations.
Inherit: Keep MSB — Simulink chooses a data type that maintains the full range of the operation, and then reduces the precision to a size appropriate for the embedded target hardware. This rule never produces overflows.

Tip
For more efficient generated code, clear the Saturate on integer overflow parameter.
Inherit: Match scaling — Simulink chooses a data type whose scaling matches the scaling of the input types, where the input refers to an input of a block under the mask. If the full range of the type does not fit on the embedded target hardware, the software reduces the range to yield a type that is appropriate for the embedded target hardware. This rule can produce overflows.
The software cannot simultaneously optimize code efficiency and numerical accuracy in all situations. If these internal rules do not meet your specific needs for numerical accuracy or performance, use one of the following options:
- Specify the data type explicitly.
- Specify a default data type explicitly, such as fixdt(1,32,16), and then use the Fixed-Point Tool to propose data types for your model. For more information, see fxptdlg (Fixed-Point Designer).
Inherit: Inherit via back propagation — (Not recommended) The block uses the data type of a downstream block under the mask.
Inherit: Same as first input — (Not recommended) This first input refers to the first input of a block under the mask.

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

Programmatic Use

Block Parameter: KernelDataTypeStr

Type: character vector

Values:

'Inherit: Inherit via
                        internal rule

Default: 'double'

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

Lower value of the kernel computation internal variable range that Simulink checks.

Simulink uses the minimum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Kernel minimum parameter does not saturate or clip the actual kernel computation value signal.

Programmatic Use

Block Parameter: KernelOutMin

Type: character vector

Values: '[ ]'| scalar

Default: '[ ]'

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

Upper value of the kernel computation internal variable range that Simulink checks.

Simulink uses the maximum value to perform:

Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink)) for some blocks.
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 Kernel maximum parameter does not saturate or clip the actual kernel computation value signal.

Programmatic Use

Block Parameter: KernelOutMax

Type: character vector

Values: '[ ]'| scalar

Default: '[ ]'

Model Examples

Predict Class Labels Using ClassificationSVM Predict Block

Use the ClassificationSVM Predict block for label prediction. The block accepts an observation (predictor data) and returns the predicted class label and class score for the observation using a trained support vector machine (SVM) classification model.

Open Live Script

More About

expand all

Classification Score

The SVM classification score for classifying observation x is the signed distance from x to the decision boundary ranging from -∞ to +∞. A positive score for a class indicates that x is predicted to be in that class. A negative score indicates otherwise.

The positive class classification score $f (x)$ is the trained SVM classification function. $f (x)$ is also the numerical predicted response for x, or the score for predicting x into the positive class.

$f (x) = \sum_{j = 1}^{n} α_{j} y_{j} G (x_{j}, x) + b,$

where $(α_{1}, ..., α_{n}, b)$ are the estimated SVM parameters, $G (x_{j}, x)$ is the dot product in the predictor space between x and the support vectors, and the sum includes the training set observations. The negative class classification score for x, or the score for predicting x into the negative class, is –f(x).

If G(x_j,x) = x_j′x (the linear kernel), then the score function reduces to

$f (x) = (x / s)' β + b .$

s is the kernel scale and β is the vector of fitted linear coefficients.

For more details, see Understanding Support Vector Machines.

Posterior Probability

The posterior probability is the probability that an observation belongs in a particular class, given the data.

For SVM, the posterior probability is a function of the score P(s) that observation j is in class k = {-1,1}.

For separable classes, the posterior probability is the step function

$P (s_{j}) = {\begin{cases} \begin{matrix} 0; & s < \max_{y_{k} = - 1} s_{k} \end{matrix} \\ \begin{matrix} π; & \max_{y_{k} = - 1} s_{k} \leq s_{j} \leq \min_{y_{k} = + 1} s_{k} \end{matrix} \\ \begin{matrix} 1; & s_{j} > \min_{y_{k} = + 1} s_{k} \end{matrix} \end{cases},$
where:
- s_j is the score of observation j.
- +1 and –1 denote the positive and negative classes, respectively.
- π is the prior probability that an observation is in the positive class.
For inseparable classes, the posterior probability is the sigmoid function

$P (s_{j}) = \frac{1}{1 + \exp (A s_{j} + B)},$
where the parameters A and B are the slope and intercept parameters, respectively.

Prior Probability

The prior probability of a class is the assumed relative frequency with which observations from that class occur in a population.

Tips

If you are using a linear SVM model and it has many support vectors, then prediction (classifying observations) can be slow. To efficiently classify observations based on a linear SVM model, remove the support vectors from the ClassificationSVM object by using discardSupportVectors.

Alternative Functionality

You can use a MATLAB Function block with the predict object function of the ClassificationSVM object. For an example, see Predict Class Labels Using MATLAB Function Block.

When deciding whether to use the ClassificationSVM 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.

Documentation

ClassificationSVM Predict

Description

Ports

Input

X — Predictor data row vector | column vector

Dependencies

Output

Label — Predicted class label scalar

Dependencies

Score — Predicted class scores or posterior probabilities scalar | 1-by-2 vector

Dependencies

Parameters

Main

Select trained machine learning model — SVM classification model svmMdl (default) | ClassificationSVM object | CompactClassificationSVM 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 — Prevent fixed-point tools from overriding data type off (default) | on

Programmatic Use

Programmatic Use

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

Programmatic Use

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

Programmatic Use

Score data type — Data type of Score output double (default) | Inherit: auto | single | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | fixdt(1,16) | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Programmatic Use

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

Programmatic Use

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

Programmatic Use

Raw score data type — Untransformed score data type double (default) | Inherit: auto | single | int8 | uint8 | int16 | uint16 | int32 | uint32 | int64 | uint64 | fixdt(1,16) | fixdt(1,16,0) | fixdt(1,16,2^0,0) | <data type expression>

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

Model Examples

Predict Class Labels Using ClassificationSVM Predict Block

More About

Classification Score

Posterior Probability

Prior Probability

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™.

See Also

Objects

Functions

Topics

Statistics and Machine Learning Toolbox Documentation

Support

`X` — Predictor data
row vector | column vector

`Label` — Predicted class label
scalar

`Score` — Predicted class scores or posterior probabilities
scalar | 1-by-2 vector

`Select trained machine learning model` — SVM classification model
`svmMdl` (default) | `ClassificationSVM` object | `CompactClassificationSVM` 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` — Prevent fixed-point tools from overriding data type
`off` (default) | `on`

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

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

`Score data type` — Data type of Score output
`double` (default) | `Inherit: auto` | `single` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `uint32` | `int64` | `uint64` | `fixdt(1,16)` | `fixdt(1,16,0)` | `fixdt(1,16,2^0,0)` | `<data type expression>`

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

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

`Raw score data type` — Untransformed score data type
`double` (default) | `Inherit: auto` | `single` | `int8` | `uint8` | `int16` | `uint16` | `int32` | `uint32` | `int64` | `uint64` | `fixdt(1,16)` | `fixdt(1,16,0)` | `fixdt(1,16,2^0,0)` | `<data type expression>`

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

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

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

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

C/C++ Code Generation
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Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.