Train a PredictorFunction:

Visualize the probability density for a given example:

The value with the highest probability density is predicted:

No prediction is made if the maximum probability density is below a specified threshold:

Update the value of the threshold permanently:

Train a predictor with two input features:

Get the prediction for an example that has a missing value:

Set the missing value synthesis to replace missing variables with their most likely value given known values (which is the default behavior):

Replace missing variables with random samples conditioned on known values:

Averaging over many random imputations is usually the best strategy and allows obtaining the uncertainty caused by the imputation:

Specify a learning method during training to control how the distribution of data is learned:

Predict an example with missing values using the "KernelDensityEstimation" distribution to condition values:

Provide an existing LearnedDistribution at training to use it when imputing missing values during training and later evaluations:

Specify an existing LearnedDistribution to synthesize missing values for an individual evaluation:

Control both the learning method and the evaluation strategy by passing an association at training:

Train a predictor:

Compute the prediction:

Compute the predictive distribution:

Temporarily set a recalibration function to apply to the prediction:

Compute the predictive distribution with this new recalibration:

Load the Boston Homes dataset:

Train a predictor with model calibration:

Visualize the comparison plot on a test set:

Remove the recalibration function from the predictor:

Visualize the new comparison plot:

Train a predictor using a neural network:

Evaluate the resulting predictor on system's default GPU and look at its AbsoluteTiming:

Compare the previous timing with the one achieved by using the default CPU computation:

Train a predictor function:

Visualize the probability density for a given example:

By default, the value with the highest probability density is predicted:

Define a utility function that penalizes the predicted value's being smaller than the actual value:

Plot this function for a given actual value:

The predictor decision is now changed despite the probability density's being unchanged:

Update the value of the utility function permanently:

Obtain the built-in PredictorFunction "NameAge":

Set a custom utility function to use with this predictor:

Compute the time required to use an example with this utility function:

Use PerformanceGoal to use a faster but less accurate result: