Undefined function 'loss' for input arguments of type 'TreeBagger'.

Undefined function 'loss' for input arguments of type 'TreeBagger'.

Error in sample (line 23)

test_accuracy = 1 - loss(rf_classifier, X_test, y_test);

Please suggest me how to fix this error.

Sanchit

[Jayant]

What is the loss function you are using?

[Sandeep Mishra]

@Sanchit

Can you share your code with required attachments?

Because it seems like you are not using the correct input paramerters

Refer to the documentation to learn more about input parameters

https://in.mathworks.com/help/stats/classreg.learning.classif.compactclassificationsvm.loss.html

Answer 1

[Satwik Samayamantry]

Hi Sanchit,

The input argument of treebagger may not agree with the input arguments of the loss functions inbuilt in matlab. Please see the input arguments of the loss function you are using and try to use a compatible data type for the same.

Answer 2

[Anuj]

Hi Sanchit,

The error message can be of because you are using wrong syntax for the loss function which is not meant for the TreeBagger. To fix this error, you can use the oobLoss method of the TreeBagger object to calculate the out-of-bag loss.

% Train the random forest classifier
rf_classifier = TreeBagger(num_trees, X_train, y_train);

% Predict the labels for the test data
y_pred = predict(rf_classifier, X_test);

% Calculate the test accuracy
test_accuracy = sum(strcmp(y_pred, y_test)) / numel(y_test);

In the above code, after predicting the labels. I have compared the predicted labels with the actual ones and finding loss in the same way.

Alternatively, if you specifically want to calculate the misclassification error. You can have a look at this code

% Train the random forest classifier
rf_classifier = TreeBagger(num_trees, X_train, y_train);

% Predict the labels for the test data
y_pred = predict(rf_classifier, X_test);

% Calculate the misclassification error
misclassification_error = sum(~strcmp(y_pred, y_test)) / numel(y_test);

% Calculate the test accuracy
test_accuracy = 1 - misclassification_error;

The misclassification error is calculated by counting the number of labels that are not equal between y_pred and y_test, and then dividing it by the total number of test samples. The test accuracy is obtained by subtracting the misclassification error from 1.