Computes error on the training or test dataset

err = model.calcError(samples, responses)
err = model.calcError(csvFilename, [])
[err,resp] = model.calcError(...)
[...] = model.calcError(..., 'OptionName', optionValue, ...)

Input

• samples See the train method.
• responses See the train method.
• csvFilename See the train method.

Output

• err computed error.
• resp the optional output responses.

Options

• Data See the train method.
• TestError if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate an already trained classifier, you will probably want not to set the test subset at all with TrainTestSplitRatio and specify TestError=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing. default false

The method uses the predict method to compute the error. For regression models the error is computed as RMS, for classifiers as a percent of missclassified samples (0%-100%).