fit-classifier: Fit a supervised learning classifier.¶

Docstring:

Usage: qiime sample-classifier fit-classifier [OPTIONS]

  Fit a supervised learning classifier. Outputs the fit estimator (for
  prediction of test samples and/or unknown samples) and the relative
  importance of each feature for model accuracy. Optionally use k-fold cross-
  validation for automatic recursive feature elimination and hyperparameter
  tuning.

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[Categorical]
                         Numeric metadata column to use as prediction target.
                                                                    [required]
  --p-step PROPORTION Range(0.0, 1.0, inclusive_start=False)
                         If optimize-feature-selection is True, step is the
                         percentage of features to remove at each iteration.
                                                               [default: 0.05]
  --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('RandomForestClassifier',
    'ExtraTreesClassifier', 'GradientBoostingClassifier',
    'AdaBoostClassifier[DecisionTree]', 'AdaBoostClassifier[ExtraTrees]',
    'KNeighborsClassifier', 'LinearSVC', 'SVC')
                         Estimator method to use for sample prediction.
                                           [default: 'RandomForestClassifier']
  --p-optimize-feature-selection / --p-no-optimize-feature-selection
                         Automatically optimize input feature selection using
                         recursive feature elimination.       [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-sample-estimator ARTIFACT SampleEstimator[Classifier]
                         Trained sample classifier.                 [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.