Classify observations using ensemble of classification models
also returns a matrix of classification scores
scores), 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.
Predictor data to be classified, specified as a numeric matrix or table.
Each row of
Specify optional pairs of arguments as
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.
Indices of weak learners
A logical matrix of size
Indication to perform inference in parallel, specified as
Vector of classification labels.
A 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).
Predict Class Labels Using Classification Ensemble
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.
Assess Performance of Ensemble of Boosted Trees
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); tabulate(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", ... "NumLearningCycles",numTrees,"Learners",t);
mdl is a trained
Inspect the importance measure for each predictor.
predImportance = predictorImportance(mdl); bar(predImportance) title("Predictor Importance") xlabel("Predictor") 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,:)); confusionchart(Y(idxTest),predictedValues)
Compute the accuracy of the model on the test data.
error = loss(mdl,X(idxTest,:),Y(idxTest), ... "LossFun","classiferror"); accuracy = 1 - error
accuracy = 0.7731
accuracy estimates the fraction of correctly classified observations.
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:
Bagscores range from
1. You can interpret these scores as probabilities averaged over all the trees in the ensemble.
LogitBoostscores range from –∞ to ∞. You can convert these scores to probabilities by setting the
predict:Alternatively, you can specify
Mdl.ScoreTransform = 'doublelogit'; [labels,scores] = predict(Mdl,X);
'ScoreTransform','doublelogit'in the call to
fitcensemblewhen you create
For more information on the different ensemble algorithms and how they compute scores, see Ensemble Algorithms.
Calculate with arrays that have more rows than fit in memory.
Usage notes and limitations:
You cannot use
UseParallelwith tall arrays.
For more information, see Tall Arrays.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
codegen(MATLAB Coder) to generate code for the
predictfunction. Save a trained model by using
saveLearnerForCoder. Define an entry-point function that loads the saved model by using
loadLearnerForCoderand calls the
predictfunction. Then use
codegento generate code for the entry-point function.
To generate single-precision C/C++ code for
predict, specify the name-value argument
"DataType","single"when you call the
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
loadLearnerForCoderin an entry-point function. Generating fixed-point C/C++ code requires MATLAB Coder™ and Fixed-Point Designer™.
Generating fixed-point code for
predictincludes 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
For the usage notes and limitations of the model object, see Code Generation of the
For general code generation,
Xmust be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both.
For fixed-point code generation,
Xmust be a fixed-point matrix.
The number of rows, or observations, in
Xcan be a variable size, but the number of columns in
Xmust be fixed.
If you want to specify
Xas a table, then your model must be trained using a table, and your entry-point function for prediction must do the following:
Accept data as arrays.
Create a table from the data input arguments and specify the variable names in the table.
Pass the table to
For an example of this table workflow, see Generate Code to Classify 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).
Name-value pair arguments
Names in name-value arguments must be compile-time constants. For example, to allow user-defined indices up to 5 weak learners in the generated code, include
For fixed-point code generation, the
'Learners'value must have an integer data type.
For more information, see Introduction to Code Generation.
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, set the
UseParallel name-value argument to
true in the call to this function.
For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).
You cannot use
UseParallel with tall or GPU arrays or in code generation.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
predictfunction does not support ensembles trained using decision tree learners with surrogate splits.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Introduced in R2011a