ClassificationLinear Predict
Libraries:
Statistics and Machine Learning Toolbox /
Classification
Description
The ClassificationLinear Predict block classifies observations using a
linear classification object (ClassificationLinear
) for binary
classification.
Import a trained 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 predicted class labels for the observation. You can add the optional output port score, which returns predicted class scores or posterior probabilities.
Examples
Predict Class Labels Using ClassificationLinear Predict Block
Use the ClassificationLinear Predict block for label prediction in Simulink®. The block accepts an observation (predictor data) and returns the predicted class label and class score for the observation using the trained classification linear model.
- Since R2023a
- 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
label — Predicted class label
scalar
Predicted class label, returned as a scalar. label is the
class yielding the highest score. For more details, see the Label
argument of the predict
object function.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
| enumerated
score — Predicted class scores or posterior probabilities
row vector
Predicted class scores or posterior probabilities, returned as a 1-by-2 row
vector. If the model was trained using a logistic learner, the classification scores
are posterior probabilities. The classification score score(i)
represents the posterior probability that the observation in x
belongs to class i
.
To check the order of the classes, use the ClassNames
property of the linear model specified by Select trained machine
learning model.
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.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Parameters
Main
Select trained machine learning model — Linear classification model
linearMdl
(default) | ClassificationLinear
object
Specify the name of a workspace variable that contains a ClassificationLinear
object.
When you train the model by using fitclinear
, 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 (double
orsingle
). Also, you cannot use theCategoricalPredictors
name-value argument. To include categorical predictors in a model, preprocess them by usingdummyvar
before fitting the model.The
Lambda
property (regularization term strength) of the trained model must be a numeric scalar. IfLambda
is a numeric vector, you must select the model corresponding to one regularization strength by usingselectModels
.The value of the
ScoreTransform
name-value argument cannot be"invlogit"
or an anonymous function.
Programmatic Use
Block Parameter:
TrainedLearner |
Type: workspace variable |
Values:
ClassificationLinear object |
Default:
'linearMdl' |
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 ClassificationLinear Predict block.
Programmatic Use
Block Parameter:
ShowOutputScore |
Type: character vector |
Values:
'off' | 'on' |
Default:
'off' |
Data Types
Fixed-Point Operational ParametersInteger 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 |
---|---|---|---|
Select this check box
( | 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 |
Clear this check box
( | 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 |
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" |
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 ismyMdl
, and the class labels areclass 1
andclass 2
. Then, the corresponding label values aremyMdl_enumLabels.class_1
andmyMdl_enumLabels.class_2
. The block converts the class labels to valid MATLAB identifiers by using thematlab.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" | "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>" |
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).
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:
ScoreDataTypeStr |
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)"
| "<data type expression>" |
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).
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).
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 |
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)"
| "<data type expression>" |
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: "[]" |
Inner product data type — Inner product data type
Inherit: Inherit via internal
rule
(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 inner product term of the classification
score. The type can be inherited, 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 inner product 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).
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:
InnerProductDataTypeStr |
Type: character vector |
Values: 'Inherit: Inherit via
internal rule' | '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>' |
Default: 'Inherit: Inherit via
internal rule' |
Inner product data type Minimum — Minimum of inner product term for range checking
[]
(default) | scalar
Specify the lower value of the inner product term 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 Inner product data type Minimum parameter does not saturate or clip the actual inner product term value.
Programmatic Use
Block Parameter:
InnerProductOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Inner product data type Maximum — Maximum of inner product term for range checking
[]
(default) | scalar
Specify the upper value of the inner product term 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 Inner product data type Maximum parameter does not saturate or clip the actual inner product term value.
Programmatic Use
Block Parameter:
InnerProductOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
More About
Classification Score
For linear classification models, the raw classification score for classifying the observation x into the positive class is defined by
f(x) = xβ+b
β is the estimated column vector of coefficients, and
b is the estimated scalar bias. The linear classification model object
specified by Select trained machine learning
model contains the coefficients and bias in the Beta
and
Bias
properties, respectively.
The raw classification score for classifying x into the negative class is –f(x). The software classifies observations into the class that yields the positive score.
If the linear classification model uses no score transformations, then the raw
classification score is the same as the classification score. If the model consists of
logistic regression learners, then the software applies the 'logit'
score
transformation to the raw classification scores.
You can specify the data types for the components required to compute classification scores using Score data type, Raw score data type, and Inner product data type.
Score data type determines the data type of the classification score.
Raw score data type determines the data type of the raw classification score f if the model uses a score transformation other than
'none'
or'identity'
.Inner product data type determines the data type of xβ.
Alternative Functionality
You can use a MATLAB Function block with the predict
object function of a linear classification object (ClassificationLinear
). For an example, see Predict Class Labels Using MATLAB Function Block.
When deciding whether to use the ClassificationLinear 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 R2023a
See Also
Blocks
- ClassificationECOC Predict | ClassificationSVM Predict | RegressionLinear Predict | IncrementalClassificationLinear Predict | IncrementalClassificationLinear Fit
Objects
Functions
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