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

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

Input

• samples Input row vectors (one or more) stored as rows of a floating-point matrix.

Output

• results Output labels or regression values.
• f The same as the response of the first sample.

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. default false
• CompressedInput compressed data, containing only the active samples/variables. default false
• PreprocessedInput This parameter is normally set to false, implying a regular input. If it is true, the method assumes that all the values of the discrete input variables have been already normalized to 0..NCategories ranges since the decision tree uses such normalized representation internally. It is useful for faster prediction with tree ensembles. For ordered input variables, the flag is not used. Default false
• PredictAuto Setting this to true, overrides all of the other Predict* flags. It automatically chooses between PredictSum and PredictMaxVote (if the model is a regressor or the number of classes are 2 with RawOutput set then it picks PredictSum, otherwise it picks PredictMaxVote by default). default true
• PredictSum If true then return sum of votes instead of the class label. default false
• PredictMaxVote If true then return the class label with the max vote. default false

The method runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting.