# ClassificationTree Predict

Classify observations using decision tree classifier

Since R2021a

Libraries:
Statistics and Machine Learning Toolbox / Classification

## Description

The ClassificationTree Predict block classifies observations using a classification tree object (`ClassificationTree` or `CompactClassificationTree`) for multiclass 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 a predicted class label for the observation. You can add the optional output port score, which returns predicted class scores or posterior probabilities.

## Ports

### Input

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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

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Predicted class label, returned as a scalar. The predicted class is the class that minimizes the expected classification cost. For more details, see the More About section of the `predict` function reference page.

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

Predicted class scores or posterior probabilities, returned as a row vector of size 1-by-k, where k is the number of classes in the tree model.

The classification score of a leaf node is the posterior probability of the classification at the node. The posterior probability of the classification at a node is the number of training observations that lead to the node with the classification, divided by the number of training observations that lead to the node.

To check the order of the classes, use the `ClassNames` property of the tree 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

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### Main

Specify the name of a workspace variable that contains a `ClassificationTree` object or `CompactClassificationTree` object.

When you train the model by using `fitctree`, 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.

• You cannot use surrogate splits; that is, the value of the `Surrogate` name-value argument must be `'off'` (default).

#### Programmatic Use

 Block Parameter: `TrainedLearner` Type: workspace variable Values: `ClassificationTree` object | `CompactClassificationTree` object Default: `'treeMdl'`

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

#### Programmatic Use

 Block Parameter: `ShowOutputScore` Type: character vector Values: `'off' | 'on'` Default: `'off'`

### Data Types

Fixed-Point Operational Parameters

Specify the rounding mode for fixed-point operations. For more information, see Rounding (Fixed-Point Designer).

Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression 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.

ActionRationaleImpact on OverflowsExample

Select this check box (`on`).

Your model has possible overflow, and you want explicit saturation protection in the generated code.

Overflows saturate to either the minimum or maximum value that the data type can represent.

The maximum value that the `int8` (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box selected, the block output saturates at 127. Similarly, the block output saturates at a minimum output value of –128.

Clear this check box (`off`).

You want to optimize efficiency of your generated code.

You want to avoid overspecifying how a block handles out-of-range signals. For more information, see Troubleshoot Signal Range Errors (Simulink).

Overflows wrap to the appropriate value that the data type can represent.

The maximum value that the `int8` (signed 8-bit integer) data type can represent is 127. Any block operation result greater than this maximum value causes overflow of the 8-bit integer. With the check box cleared, the software interprets the 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'`

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 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.

Click the 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)'` | `'fixdt(1,16,0)'` | `'fixdt(1,16,2^0,0)'` | ```'Enum: '``` | ```''``` Default: ```'Inherit: Inherit via back propagation'``` (for numeric and logical labels) | `'Inherit: auto'` (for nonnumeric labels)

Specify the lower value of the label output range that Simulink checks.

Simulink uses the minimum value to perform:

Note

The Label 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: `'[]'`

Specify the upper value of the label output range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Label 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: `'[]'`

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.

Click the 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)'` | `'fixdt(1,16,0)'` | `'fixdt(1,16,2^0,0)'` | ```''``` Default: ```'Inherit: auto'```

Specify the lower value of the score output range that Simulink checks.

Simulink uses the minimum value to perform:

Note

The Score minimum parameter does not saturate or clip the actual score signal. To do so, use the Saturation (Simulink) block instead.

#### Programmatic Use

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

Specify the upper value of the score output range that Simulink checks.

Simulink uses the maximum value to perform:

Note

The Score maximum parameter does not saturate or clip the actual score signal. To do so, use the Saturation (Simulink) block instead.

#### Programmatic Use

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

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.

Click the 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 changing 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)'` | `'fixdt(1,16,0)'` | `'fixdt(1,16,2^0,0)'` | ```''``` Default: `'Inherit: auto'`

Specify the lower value of the untransformed score range that Simulink checks.

Simulink uses the minimum value to perform:

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: `'[]'`

Specify the upper value of the untransformed score range that Simulink checks.

Simulink uses the maximum value to perform:

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: `'[]'`

## 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`

## Alternative Functionality

You can use a MATLAB Function block with the `predict` object function of a classification tree object (`ClassificationTree` or `CompactClassificationTree`). For an example, see Predict Class Labels Using MATLAB Function Block.

When deciding whether to use the ClassificationTree 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.

## Version History

Introduced in R2021a