Predict

Predict[{in1out1,in2out2,}]
generates a PredictorFunction[] based on the example input-output pairs given.

Predict[{in1,in2,}{out1,out2,}]
generates the same result.

Predict[training,input]
attempts to predict the output associated with input from the training examples given.

Predict["name",input]
uses the built-in predictor function represented by .

Predict["name",input,prop]
gives the specified property of the prediction associated with input.

Details and OptionsDetails and Options

  • Predict works for a variety of data types, including numerical, textual, sound, and image.
  • Each can be a single feature, a list of features, or an association of features. When is a list of features, all must have the same dimensions.
  • Predict[training] returns a PredictorFunction[] that can then be applied to specific data.
  • In Predict[,input], input can be a single item or a list of items.
  • In Predict[,input,prop], properties are as given in PredictorFunction[].
  • Examples of built-in predictor functions include:
  • "NameAge"age of a person, given their first name
  • The following options can be given:
  • IndeterminateThreshold0below what probability density to return Indeterminate
    MethodAutomaticwhich regression algorithm to use
    NominalVariablesAutomaticwhich features should be considered categorical
    PerformanceGoalAutomaticfavor algorithms with specific advantages
    UtilityFunctionAutomaticutility expressed as a function of actual and predicted value
    ValidationSetAutomaticdata on which to validate the model generated
  • Possible settings for PerformanceGoal include:
  • "Memory"minimize the storage requirements of the predictor
    "Quality"maximize the accuracy of the predictor
    "Speed"maximize the speed of the predictor
    "TrainingSpeed"minimize the time spent producing the predictor
  • PerformanceGoal->{goal1,goal2,} will automatically combine , , etc.
  • Possible settings for Method include:
  • "LinearRegression"predict from linear combinations of features
    "NearestNeighbors"predict from nearest neighboring examples
    "NeuralNetwork"predict using an artificial neural network
    "RandomForest"predict from BreimanCutler ensembles of decision trees

ExamplesExamplesopen allclose all

Basic Examples  (2)Basic Examples  (2)

Train a predictor function on a set of examples:

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Predict the value of a new example, given its feature:

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Get the conditional distribution of the value, given the example feature:

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Plot this distribution:

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Predict multiple examples:

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Plot the predicted values as a function of the feature value and show the training examples:

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Train a predictor on a dataset with multiple features:

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Predict the value of a new example, given its features:

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Predict the value of a new example that has a missing feature:

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Introduced in 2014
(10.0)