represents a function generated by Predict that predicts numerical values from data.

Details and Options

  • PredictorFunction works like Function.
  • PredictorFunction[][data] attempts to predict the value associated with data.
  • PredictorFunction[][{data1,data2,}] attempts to predict all the datai.
  • PredictorFunction[][data,prop] gives the specified property of the prediction associated with data.
  • Possible properties applicable to all methods include:
  • "Decision"best prediction according to the distribution and the utility function
    "Distribution"distribution of value conditioned on input
    "Properties"list of all properties available
  • PredictorFunction[][data,,opts] specifies that the predictor should use the options opts when applied to data.
  • Possible options are:
  • IndeterminateThresholdAutomaticbelow what probability density to return Indeterminate
    PerformanceGoalAutomaticwhich aspect of performance to optimize
    TargetDevice"CPU"the target device on which to perform training
    UtilityFunctionAutomaticutility expressed as a function of actual and predicted value
  • Predict[PredictorFunction[],opts] can be used to update the values of PerformanceGoal, IndeterminateThreshold, UtilityFunction or FeatureExtractor of the classifier.
  • In Predict[PredictorFunction[],FeatureExtractorfe], the FeatureExtractorFunction[] fe will be prepended to the existing feature extractor.
  • Information[PredictorFunction[]] generates an information panel about the classifier and its estimated performances.
  • Information[PredictorFunction[],prop] can be used to obtain specific properties.
  • Information of a PredictorFunction[] may include the following properties:
  • "BatchEvaluationTime"marginal time to predict one example when a batch is given
    "EvaluationTime"time needed to predict one example
    "ExampleNumber"number of training examples
    "FeatureTypes"feature types of the predictor input
    "FunctionMemory"memory needed to store the predictor
    "FunctionProperties"all prediction properties available for this predictor
    "IndeterminateThreshold"value of IndeterminateThreshold used by the predictor
    "LearningCurve"performance as function of training set size
    "MaxTrainingMemory"maximum memory used during training
    "MeanCrossEntropy"estimated mean cross entropy of the predictor
    "Method"value of Method used by the predictor
    "MethodDescription"summary of the method
    "MethodOption"full method option to be reused in a new training
    "Properties"all information properties available for this predictor
    "StandardDeviation"estimated standard deviation of the predictor
    "TrainingTime"time used by Predict to generate the predictor
    "UtilityFunction"value of UtilityFunction used by the predictor
  • Information properties also include all method suboptions.


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Basic Examples  (2)

Train a PredictorFunction:

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Use the trained PredictorFunction to predict an output, given some feature:

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Predict multiple examples:

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Calculate the probability distribution of the predicted value:

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Generate a PredictorFunction using multiple features:

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Use the function on a new example:

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Predict an example that has missing features:

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Obtain general information about the predictor:

Click for copyable input

Scope  (5)

Options  (4)

Introduced in 2014
Updated in 2018