predict

Classify observations using ensemble of classification models

Syntax

labels = predict(Mdl,X)

labels = predict(Mdl,X,Name,Value)

[labels,score]
= predict(___)

Description

labels = predict(Mdl,X) returns a vector of predicted class labels for the predictor data in the table or matrix X, based on the full or compact, trained classification ensemble Mdl.

labels = predict(Mdl,X,Name,Value) uses additional options specified by one or more Name,Value pair arguments.

[labels,score] = predict(___) also returns a matrix of classification scores (score), indicating the likelihood that a label comes from a particular class, using any of the input arguments in the previous syntaxes. For each observation in X, the predicted class label corresponds to the maximum score among all classes.

Input Arguments

Mdl

A classification ensemble created by fitcensemble or a compact classification ensemble created by compact.

X

Predictor data to be classified, specified as a numeric matrix or table.

Each row of X corresponds to one observation, and each column corresponds to one variable.

For a numeric matrix:
- The variables making up the columns of X must have the same order as the predictor variables that trained Mdl.
- If you trained Mdl using a table (for example, Tbl), then X can be a numeric matrix if Tbl contains all numeric predictor variables. To treat numeric predictors in Tbl as categorical during training, identify categorical predictors using the CategoricalPredictors name-value pair argument of fitcensemble. If Tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) and X is a numeric matrix, then predict throws an error.
For a table:
- predict does not support multi-column variables and cell arrays other than cell arrays of character vectors.
- If you trained Mdl using a table (for example, Tbl), then all predictor variables in X must have the same variable names and be of the same data types as those that trained Mdl (stored in Mdl.PredictorNames). However, the column order of X does not need to correspond to the column order of Tbl. Tbl and X can contain additional variables (response variables, observation weights, etc.), but predict ignores them.
- If you trained Mdl using a numeric matrix, then the predictor names in Mdl.PredictorNames and corresponding predictor variable names in X must be the same. To specify predictor names during training, see the PredictorNames name-value pair argument of fitcensemble. All predictor variables in X must be numeric vectors. X can contain additional variables (response variables, observation weights, etc.), but predict ignores them.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'Learners'

Indices of weak learners predict uses for computation of responses, a numeric vector.

Default: 1:T, where T is the number of weak learners in Mdl

'UseObsForLearner'

A logical matrix of size N-by-T, where:

N is the number of rows of X.
T is the number of weak learners in Mdl.

When UseObsForLearner(i,j) is true, learner j is used in predicting the class of row i of X.

Default: true(N,T)

Output Arguments

`labels`	Vector of classification labels. `labels` has the same data type as the labels used in training `Mdl`. (The software treats string arrays as cell arrays of character vectors.)
`score`	A matrix with one row per observation and one column per class. For each observation and each class, the score generated by each tree is the probability of this observation originating from this class computed as the fraction of observations of this class in a tree leaf. `predict` averages these scores over all trees in the ensemble.

Examples

expand all

Predict Class Labels Using Classification Ensemble

Open Live Script

Load Fisher's iris data set. Determine the sample size.

load fisheriris
N = size(meas,1);

Partition the data into training and test sets. Hold out 10% of the data for testing.

rng(1); % For reproducibility
cvp = cvpartition(N,'Holdout',0.1);
idxTrn = training(cvp); % Training set indices
idxTest = test(cvp);    % Test set indices

Store the training data in a table.

tblTrn = array2table(meas(idxTrn,:));
tblTrn.Y = species(idxTrn);

Train a classification ensemble using AdaBoostM2 and the training set. Specify tree stumps as the weak learners.

t = templateTree('MaxNumSplits',1);
Mdl = fitcensemble(tblTrn,'Y','Method','AdaBoostM2','Learners',t);

Predict labels for the test set. You trained model using a table of data, but you can predict labels using a matrix.

labels = predict(Mdl,meas(idxTest,:));

Construct a confusion matrix for the test set.

confusionchart(species(idxTest),labels)

Mdl misclassifies one versicolor iris as virginica in the test set.

More About

expand all

Score (ensemble)

For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.

Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:

AdaBoostM1 scores range from –∞ to ∞.
Bag scores range from 0 to 1.

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.

For more information, see Tall Arrays.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

Use saveLearnerForCoder, loadLearnerForCoder, and codegen (MATLAB Coder) to generate code for the predict function. Save a trained model by using saveLearnerForCoder. Define an entry-point function that loads the saved model by using loadLearnerForCoder and calls the predict function. Then use codegen to generate code for the entry-point function.
You can also generate single-precision C/C++ code for predict. For single-precision code generation, specify the name-value pair argument 'DataType','single' as an additional input to the loadLearnerForCoder function.
You can also generate fixed-point C/C++ code for predict. Fixed-point code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using the data type function generated by generateLearnerDataTypeFcn, and use the structure as an input argument of loadLearnerForCoder in an entry-point function. Generating fixed-point C/C++ code requires MATLAB^® Coder™ and Fixed-Point Designer™.
Generating fixed-point code for predict includes propagating data types for individual learners and, therefore, can be time consuming.

This table contains notes about the arguments of predict. Arguments not included in this table are fully supported.

Argument	Notes and Limitations
`Mdl`	For the usage notes and limitations of the model object, see Code Generation of the `CompactClassificationEnsemble` object.
`X`	For general code generation, `X` must be a single-precision or double-precision matrix or a table containing `single` or `double` predictor variables. If you want to specify `X` as a table, then your model must be trained using a table, and you must ensure that your entry-point function for prediction: Accepts data as arrays Creates a table from the data input arguments and specifies the variable names in the table Passes the table to `predict` For an example of this table workflow, see Generate Code to Classify Numeric Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder). For fixed-point code generation, `X` must be a fixed-point matrix. The number of rows, or observations, in `X` can be a variable size, but the number of columns in `X` must be fixed.
Name-value pair arguments	Names in name-value pair arguments must be compile-time constants. For example, to allow user-defined indices up to 5 weak learners in the generated code, include `{coder.Constant('Learners'),coder.typeof(0,[1,5],[0,1])}` in the `-args` value of `codegen` (MATLAB Coder).
`'Learners'`	For fixed-point code generation, the `'Learners'` value must have an integer data type.

For more information, see Introduction to Code Generation.

Documentation

predict

Syntax

Description

Input Arguments

Name-Value Pair Arguments

Output Arguments

Examples

Predict Class Labels Using Classification Ensemble

More About

Score (ensemble)

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

See Also

Statistics and Machine Learning Toolbox Documentation

Support

Documentation

predict

Syntax

Description

Input Arguments

Name-Value Pair Arguments

Output Arguments

Examples

Predict Class Labels Using Classification Ensemble

More About

Score (ensemble)

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

See Also

Statistics and Machine Learning Toolbox Documentation

Support

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.