regress-samples-ncv: Nested cross-validated supervised learning regressor.¶

Docstring:

Usage: qiime sample-classifier regress-samples-ncv [OPTIONS]

  Predicts a continuous sample metadata column using a supervised learning
  regressor. Uses nested stratified k-fold cross validation for automated
  hyperparameter optimization and sample prediction. Outputs predicted values
  for each input sample, and relative importance of each feature for model
  accuracy.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency | RelativeFrequency |
    PresenceAbsence | Composition]
                         Feature table containing all features that should be
                         used for target prediction.                [required]
Parameters:
  --m-metadata-file METADATA
  --m-metadata-column COLUMN  MetadataColumn[Numeric]
                         Numeric metadata column to use as prediction target.
                                                                    [required]
  --p-cv INTEGER         Number of k-fold cross-validations to perform.
    Range(1, None)                                                [default: 5]
  --p-random-state INTEGER
                         Seed used by random number generator.      [optional]
  --p-n-jobs NTHREADS    Number of jobs to run in parallel.       [default: 1]
  --p-n-estimators INTEGER
    Range(1, None)       Number of trees to grow for estimation. More trees
                         will improve predictive accuracy up to a threshold
                         level, but will also increase time and memory
                         requirements. This parameter only affects ensemble
                         estimators, such as Random Forest, AdaBoost,
                         ExtraTrees, and GradientBoosting.      [default: 100]
  --p-estimator TEXT Choices('RandomForestRegressor',
    'ExtraTreesRegressor', 'GradientBoostingRegressor',
    'AdaBoostRegressor[DecisionTree]', 'AdaBoostRegressor[ExtraTrees]',
    'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')
                         Estimator method to use for sample prediction.
                                            [default: 'RandomForestRegressor']
  --p-stratify / --p-no-stratify
                         Evenly stratify training and test data among
                         metadata categories. If True, all values in column
                         must match at least two samples.     [default: False]
  --p-parameter-tuning / --p-no-parameter-tuning
                         Automatically tune hyperparameters using random grid
                         search.                              [default: False]
  --p-missing-samples TEXT Choices('error', 'ignore')
                         How to handle missing samples in metadata. "error"
                         will fail if missing samples are detected. "ignore"
                         will cause the feature table and metadata to be
                         filtered, so that only samples found in both files
                         are retained.                      [default: 'error']
Outputs:
  --o-predictions ARTIFACT SampleData[RegressorPredictions]
                         Predicted target values for each input sample.
                                                                    [required]
  --o-feature-importance ARTIFACT FeatureData[Importance]
                         Importance of each input feature to model accuracy.
                                                                    [required]
Miscellaneous:
  --output-dir PATH      Output unspecified results to a directory
  --verbose / --quiet    Display verbose output to stdout and/or stderr
                         during execution of this action. Or silence output if
                         execution is successful (silence is golden).
  --example-data PATH    Write example data and exit.
  --citations            Show citations and exit.
  --use-cache DIRECTORY  Specify the cache to be used for the intermediate
                         work of this action. If not provided, the default
                         cache under $TMP/qiime2/ will be used.
                         IMPORTANT FOR HPC USERS: If you are on an HPC system
                         and are using parallel execution it is important to
                         set this to a location that is globally accessible to
                         all nodes in the cluster.
  --help                 Show this message and exit.