The name of the column containing the prediction interval for each prediction when it has been passed as an argument.
Specifies the type of strategy that a model or models in an ensemble will follow when a missing value needed to continue with inference in the model is found.
Whether a unique id for the final tree node for each tree prediction was added to the output file.
Whether the vote_count, the fraction of ensembles voting for the given prediction, for each prediction for the classification ensemble task should be added.
The name of the column containing the predictions when it has been passed as an argument.
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The negative class that will be returned when the model does not reach the confidence_threshold or probability_threshold level on the positive_class.
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Whether a column containing a unique id for the final tree node when making a prediction should be added.
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When the probability is below the threshold for the positive_class, the following class with higher probability will be predicted instead.
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The minimum level of probability on the positive class that boosted trees need to reach to return the positive_class.
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The positive_class will only be predicted when the probability of the prediction for that class is above the established threshold.
Whether all the fields from the dataset should be part of the generated csv file together with the predictions.
The name of the column in the header of the generated file containing the prediction interval when a linear regression is used to create the batch prediction.
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Make sure that you check the code that comes with the status attribute to make sure that the batch prediction creation has been completed without errors.
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Whether the vote_counts, a list of fractions of votes, one per category for the batch prediction for the ensemble classification task should be added.
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Whether to include the predicted class and all other possible class values for the batch prediction for the classification task.
The class that will be returned when a confidence threshold is used and the threshold is not reached for the model or ensemble.
Whether to include a column per class with its corresponding confidence for the batch prediction for the classification task.
The map of dataset fields to model fields used.
The probability returned for predictions that have been overridden by the negative_class when a probability_threshold has been used.
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The minimum level of confidence on the positive class that a classification model needs to reach to return the positive_class.
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Specifies the method that should be used for the model or ensemble when a missing split is found.
The name of the column containing the confidence for each prediction when it has been passed as an argument.
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The confidence returned for predictions that have been overridden by the negative_class when a confidence_threshold has been used.
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The name of the column containing the confidence interval for each prediction when it has been passed as an argument.
The name of the column containing the probability for each prediction when it has been passed as an argument.
If vote_count is set to true, then vote_count_name is used to prefix for naming those columns.
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The name of the column in the header of the generated file containing the probability for the classification task.
The name of the column in the header of the generated file containing the vote_count for the ensemble classification task.
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This is currently only available for single tree models and the ID may be null missing_strategy is 1 (proportional).
The name of the column in the header of the generated file containing the confidence interval when a linear regression is used to create the batch prediction.
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When the prediction is overridden, the new confidence returned will be 1 unless specified differently using negative_class_confidence.
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