qiime sample-classifier fit-classifier (version 2019.4)

--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]:

--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-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: :

--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: :

--m-metadata-file METADATA:

Fit a supervised learning classifier.

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.

Parameters

table : FeatureTable[Frequency]: Feature table containing all features that should be used for target prediction.
metadata : MetadataColumn[Categorical]: Numeric metadata column to use as prediction target.
step : Float % Range(0.0, 1.0, inclusive_start=False), optional: If optimize_feature_selection is True, step is the percentage of features to remove at each iteration.
cv : Int % Range(1, None), optional: Number of k-fold cross-validations to perform.
random_state : Int, optional: Seed used by random number generator.
n_estimators : Int % Range(1, None), optional: 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.
estimator : Str % Choices('RandomForestClassifier', 'ExtraTreesClassifier', 'GradientBoostingClassifier', 'AdaBoostClassifier', 'KNeighborsClassifier', 'LinearSVC', 'SVC'), optional: Estimator method to use for sample prediction.
optimize_feature_selection : Bool, optional: Automatically optimize input feature selection using recursive feature elimination.
parameter_tuning : Bool, optional: Automatically tune hyperparameters using random grid search.
missing_samples : Str % Choices('error', 'ignore'), optional: 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.

Returns

sample_estimator : SampleEstimator[Classifier]: Trained sample classifier.
feature_importance : FeatureData[Importance]: Importance of each input feature to model accuracy.