ActivePrediction

ActivePrediction[f,{conf1,conf2, }]

gives an object representing the result of active prediction obtained by using the function f to determine values for the example configurations confi.

ActivePrediction[f,reg]

generates configurations within the region specified by reg.

ActivePrediction[f,sampler]

generates configurations by applying the function sampler.

ActivePrediction[f,{conf1,conf2,}nsampler]

applies the function nsampler to successively generate configurations starting from one of the confi.

Details and Options

Examples

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Basic Examples  (3)

Train an ActivePredictionObject[] to find the predictor for a function, given a set of configurations:

Extract the resulting predictor:

Predict new examples:

Train a prediction object to find the predictor for a function whose domain is defined by an interval:

Extract the predictor:

Predict new examples:

Train a prediction object to find the predictor for the Det function, with the domain defined by a configuration generator:

Extract the predictor:

Predict new examples:

Scope  (3)

Train a prediction object to find a predictor for the sine function in an interval:

Obtain the list of available object properties:

Obtain the history of explored configurations:

Obtain the predictors trained during active prediction, along with some of their properties:

Obtain the final predictor:

Obtain the method used to choose configurations to add to the training set:

Obtain some other properties:

Display the performances of the predictors trained during active prediction:

Visualize the predictions of the predictor on new examples:

Train a prediction object to find the predictor for a function that computes the color distance between a given color and the red color, with the domain defined by a random color generator:

Obtain the predictor:

Display the performances of the predictors trained during active prediction:

Display the comparison plot of the predictor for a test set:

Define a nontrivial function, with the domain defined by a neighborhood configuration generator:

Train a prediction object to find a predictor for the function, starting with some initial configurations:

Obtain the predictor:

Visualize the predictions of the predictor. It provides a good model of the function in the neighborhood of the initial configurations:

Options  (3)

InitialEvaluationHistory  (1)

Define a quadratic function whose domain is defined by a set of numbers:

Construct an initial "training set":

Train a prediction object to find a predictor for the function using the preceding information:

The examples in the first row in the training history now correspond to the initial training set:

MaxIterations  (1)

Define a nontrivial two-dimensional function:

Train a prediction object to find a predictor for the function within a unit disk:

Obtain the number of function evaluations:

Specify the maximum number of iterations:

Check the number of function evaluations now:

Method  (1)

Define a nontrivial stochastic function:

Train a prediction object by specifying the method as an association, choosing the evaluation strategy and the prediction method:

Obtain the predictor:

Specify a different method for active prediction:

Obtain the predictor again:

Visualize the predictions of the two predictors. "GaussianProcess" produces a smoother predictor relative to "NearestNeighbors":

Applications  (2)

Efficiently train a model to predict the elevation at a specific location:

Sample 1000 random locations to test the quality of the model:

Compare the predicted elevation versus the actual elevation obtained via a server call:

LogLikelihood Function Predictor  (1)

Load Fisher's Iris dataset and divide it into a training set and a test set:

Construct a LogLikelihood function that trains a classifier on the training sample and then gives the "LogLikelihoodRate" on the test sample for a given choice of the hyperparameters:

Train a prediction object to find a predictor for the LogLikelihood function over a rectangular domain:

Obtain the predictor:

Obtain the coordinates of the explored configurations and their values:

Visualize the prediction of the predictor together with the explored configurations:

Wolfram Research (2017), ActivePrediction, Wolfram Language function, https://reference.wolfram.com/language/ref/ActivePrediction.html (updated 2017).

Text

Wolfram Research (2017), ActivePrediction, Wolfram Language function, https://reference.wolfram.com/language/ref/ActivePrediction.html (updated 2017).

CMS

Wolfram Language. 2017. "ActivePrediction." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2017. https://reference.wolfram.com/language/ref/ActivePrediction.html.

APA

Wolfram Language. (2017). ActivePrediction. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ActivePrediction.html

BibTeX

@misc{reference.wolfram_2024_activeprediction, author="Wolfram Research", title="{ActivePrediction}", year="2017", howpublished="\url{https://reference.wolfram.com/language/ref/ActivePrediction.html}", note=[Accessed: 30-May-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_activeprediction, organization={Wolfram Research}, title={ActivePrediction}, year={2017}, url={https://reference.wolfram.com/language/ref/ActivePrediction.html}, note=[Accessed: 30-May-2024 ]}