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predict-classification: Use trained classifier to predict target values for new samples.ΒΆ

Docstring:

Usage: qiime sample-classifier predict-classification [OPTIONS]

  Use trained estimator to predict target values for new samples. These will
  typically be unseen samples, e.g., test data (derived manually or from
  split_table) or samples with unknown values, but can theoretically be any
  samples present in a feature table that contain overlapping features with
  the feature table used to train the estimator.

Options:
  --i-table ARTIFACT PATH FeatureTable[Frequency]
                                  Feature table containing all features that
                                  should be used for target prediction.
                                  [required]
  --i-sample-estimator ARTIFACT PATH SampleEstimator[Classifier]
                                  Sample classifier trained with
                                  fit_classifier.  [required]
  --p-n-jobs INTEGER              Number of jobs to run in parallel.
                                  [default: 1]
  --o-predictions ARTIFACT PATH SampleData[ClassifierPredictions]
                                  Predicted target values for each input
                                  sample.  [required if not passing --output-
                                  dir]
  --output-dir DIRECTORY          Output unspecified results to a directory
  --cmd-config FILE               Use config file for command options
  --verbose                       Display verbose output to stdout and/or
                                  stderr during execution of this action.
                                  [default: False]
  --quiet                         Silence output if execution is successful
                                  (silence is golden).  [default: False]
  --citations                     Show citations and exit.
  --help                          Show this message and exit.

Import:

from qiime2.plugins.sample_classifier.methods import predict_classification

Docstring:

Use trained classifier to predict target values for new samples.

Use trained estimator to predict target values for new samples. These will
typically be unseen samples, e.g., test data (derived manually or from
split_table) or samples with unknown values, but can theoretically be any
samples present in a feature table that contain overlapping features with
the feature table used to train the estimator.

Parameters
----------
table : FeatureTable[Frequency]
    Feature table containing all features that should be used for target
    prediction.
sample_estimator : SampleEstimator[Classifier]
    Sample classifier trained with fit_classifier.
n_jobs : Int, optional
    Number of jobs to run in parallel.

Returns
-------
predictions : SampleData[ClassifierPredictions]
    Predicted target values for each input sample.