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Predict labels using classification ensemble model



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

labels = predict(ens,X,Name=Value) specifies additional options using one or more name-value arguments. For example, you can specify the weak learners to use for predictions, and perform computations in parallel.

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


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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.


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

Create an ensemble of boosted trees and inspect the importance of each predictor. Using test data, assess the classification accuracy of the ensemble.

Load the arrhythmia data set. Determine the class representations in the data.

load arrhythmia
Y = categorical(Y);
  Value    Count   Percent
      1      245     54.20%
      2       44      9.73%
      3       15      3.32%
      4       15      3.32%
      5       13      2.88%
      6       25      5.53%
      7        3      0.66%
      8        2      0.44%
      9        9      1.99%
     10       50     11.06%
     14        4      0.88%
     15        5      1.11%
     16       22      4.87%

The data set contains 16 classes, but not all classes are represented (for example, class 13). Most observations are classified as not having arrhythmia (class 1). The data set is highly discrete with imbalanced classes.

Combine all observations with arrhythmia (classes 2 through 15) into one class. Remove those observations with an unknown arrhythmia status (class 16) from the data set.

idx = (Y ~= "16");
Y = Y(idx);
X = X(idx,:);
Y(Y ~= "1") = "WithArrhythmia";
Y(Y == "1") = "NoArrhythmia";
Y = removecats(Y);

Create a partition that evenly splits the data into training and test sets.

rng("default") % For reproducibility
cvp = cvpartition(Y,"Holdout",0.5);
idxTrain = training(cvp);
idxTest = test(cvp);

cvp is a cross-validation partition object that specifies the training and test sets.

Train an ensemble of 100 boosted classification trees using AdaBoostM1. Specify to use tree stumps as the weak learners. Also, because the data set contains missing values, specify to use surrogate splits.

t = templateTree("MaxNumSplits",1,"Surrogate","on");
numTrees = 100;
mdl = fitcensemble(X(idxTrain,:),Y(idxTrain),"Method","AdaBoostM1", ...

mdl is a trained ClassificationEnsemble model.

Inspect the importance measure for each predictor.

predImportance = predictorImportance(mdl);
title("Predictor Importance")
ylabel("Importance Measure")

Identify the top ten predictors in terms of their importance.

[~,idxSort] = sort(predImportance,"descend");
idx10 = idxSort(1:10)
idx10 = 1×10

   228   233   238    93    15   224    91   177   260   277

Classify the test set observations. View the results using a confusion matrix. Blue values indicate correct classifications, and red values indicate misclassified observations.

predictedValues = predict(mdl,X(idxTest,:));

Compute the accuracy of the model on the test data.

error = loss(mdl,X(idxTest,:),Y(idxTest), ...
accuracy = 1 - error
accuracy = 0.7731

accuracy estimates the fraction of correctly classified observations.

Input Arguments

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Full classification ensemble model, specified as a ClassificationEnsemble model object trained with fitcensemble, or a CompactClassificationEnsemble model object created with compact.

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

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

For a numeric matrix:

  • The variables that make up the columns of X must have the same order as the predictor variables used to train ens.

  • If you trained ens using a table (for example, tbl), X can be a numeric matrix if tbl contains only numeric predictor variables. To treat numeric predictors in tbl as categorical during training, specify categorical predictors using the CategoricalPredictors name-value argument of fitcensemble. If tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) and X is a numeric matrix, predict issues an error.

For a table:

  • predict does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

  • If you trained ens using a table (for example, tbl), all predictor variables in X must have the same variable names and data types as those used to train ens (stored in ens.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, such as response variables and observation weights, but predict ignores them.

  • If you trained ens using a numeric matrix, then the predictor names in ens.PredictorNames must be the same as the corresponding predictor variable names in X. To specify predictor names during training, use the PredictorNames name-value argument of fitcensemble. All predictor variables in X must be numeric vectors. X can contain additional variables, such as response variables and observation weights, but predict ignores them.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: predict(ens,X,Learners=[1 2 3 5],UseParallel=true) specifies to use the first, second, third, and fifth learners in the ensemble ens, and to perform computations in parallel.

Indices of weak learners in the ensemble to use in predict, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, all learners are used.

Example: Learners=[1 2 4]

Data Types: single | double

Option to use observations for learners, specified as 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 ens.

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

Example: UseObsForLearner=logical([1 1; 0 1; 1 0])

Data Types: logical matrix

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the predict function executes for-loop iterations by using parfor. The loop runs in parallel when you have Parallel Computing Toolbox™.

Example: UseParallel=true

Data Types: logical

Output Arguments

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Predicted class labels, returned as a categorical, character, logical, or numeric array, or a cell array of character vectors. labels has the same data type as the labels used to train ens. (The software treats string arrays as cell arrays of character vectors.)

The predict function classifies an observation into the class yielding the highest score. For an observation with NaN scores, the function classifies the observation into the majority class, which makes up the largest proportion of the training labels.

Class scores, returned as a numeric matrix with one row per observation and one column per class. For each observation and each class, the score represents the confidence that the observation originates from that class. A higher score indicates a higher confidence. For more information, see Score (ensemble).

More About

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Score (ensemble)

For ensembles, a classification score represents the confidence that an observation originates from a specific 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:

  • Bag scores range from 0 to 1. You can interpret these scores as probabilities averaged over all the trees in the ensemble.

  • AdaBoostM1, GentleBoost, and LogitBoost scores range from –∞ to ∞. You can convert these scores to probabilities by setting the ScoreTransform property of ens to "doublelogit" before passing ens to predict:

    ens.ScoreTransform = "doublelogit";
    [labels,scores] = predict(ens,X);
    Alternatively, you can specify ScoreTransform="doublelogit" in the call to fitcensemble when you create ens.

For more information on the different ensemble algorithms and how they compute scores, see Ensemble Algorithms.

Alternative Functionality

Simulink Block

To integrate the prediction of an ensemble into Simulink®, you can use the ClassificationEnsemble Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB® Function block with the predict function. For examples, see Predict Class Labels Using ClassificationEnsemble Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, 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

Version History

Introduced in R2011a