Predicts response(s) for the provided sample(s)

[results,f] = model.predict(samples)
[...] = model.predict(..., 'OptionName', optionValue, ...)

Input

• samples The input samples, floating-point matrix.

Output

• results The output matrix of results.
• f If you pass one sample then prediction result is returned here, otherwise unused and returns 0. If you want to get responses for several samples then results stores all response predictions for corresponding samples.

Options

• Flags The optional predict flags, model-dependent. For convenience, you can set the individual flag options below, instead of directly setting bits here. default 0
• RawOutput makes the method return the raw results (the sum), not the class label. This flag specifies the type of the return value. If true and the problem is 2-class classification then the method returns the decision function value that is signed distance to the margin, else the function returns a class label (classification) or estimated function value (regression). default false

The function is parallelized with the TBB library.