qiime sample-classifier classify-samples (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] Categorical metadata column to use as prediction target. [required]:

--p-test-size: PROPORTION Range(0.0, 1.0, inclusive_start=False) Fraction of input samples to exclude from training set and use for classifier testing. [default: 0.2]:

--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-palette: :

--p-missing-samples: :

--m-metadata-file METADATA:

Train and test a cross-validated supervised learning classifier.

Predicts a categorical sample metadata column using a supervised learning classifier. Splits input data into training and test sets. The training set is used to train and test the estimator using a stratified k-fold cross- validation scheme. This includes optional steps for automated feature extraction and hyperparameter optimization. The test set validates classification accuracy of the optimized estimator. Outputs classification results for test set. For more details on the learning algorithm, see http://scikit-learn.org/stable/supervised_learning.html

Parameters

table : FeatureTable[Frequency]: Feature table containing all features that should be used for target prediction.
metadata : MetadataColumn[Categorical]: Categorical metadata column to use as prediction target.
test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional: Fraction of input samples to exclude from training set and use for classifier testing.
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.
palette : Str % Choices('YellowOrangeBrown', 'YellowOrangeRed', 'OrangeRed', 'PurpleRed', 'RedPurple', 'BluePurple', 'GreenBlue', 'PurpleBlue', 'YellowGreen', 'summer', 'copper', 'viridis', 'plasma', 'inferno', 'magma', 'sirocco', 'drifting', 'melancholy', 'enigma', 'eros', 'spectre', 'ambition', 'mysteriousstains', 'daydream', 'solano', 'navarro', 'dandelions', 'deepblue', 'verve', 'greyscale'), optional: The color palette to use for plotting.
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 estimator.
feature_importance : FeatureData[Importance]: Importance of each input feature to model accuracy.
predictions : SampleData[ClassifierPredictions]: Predicted target values for each input sample.
model_summary : Visualization: Summarized parameter and (if enabled) feature selection information for the trained estimator.
accuracy_results : Visualization: Accuracy results visualization.