# HG changeset patch # User q2d2 # Date 1661804923 0 # Node ID 7f28c4bde0c14ce36201621a641d5e63ee568ab4 planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 9023cfd83495a517fbcbb6f91d5b01a6f1afcda1 diff -r 000000000000 -r 7f28c4bde0c1 qiime2__sample_classifier__classify_samples.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qiime2__sample_classifier__classify_samples.xml Mon Aug 29 20:28:43 2022 +0000 @@ -0,0 +1,159 @@ + + + + + Train and test a cross-validated supervised learning classifier. + + quay.io/qiime2/core:2022.8 + + q2galaxy version sample_classifier + q2galaxy run sample_classifier classify_samples '$inputs' + + + + + + + + + hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Frequency]'] + + + + + + + + + + value != "1" + + + + + + + + + +
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+ + + + + + + + + + + + + +QIIME 2: sample-classifier classify-samples +=========================================== +Train and test a cross-validated supervised learning classifier. + + +Outputs: +-------- +:sample_estimator.qza: Trained sample estimator. +:feature_importance.qza: Importance of each input feature to model accuracy. +:predictions.qza: Predicted target values for each input sample. +:model_summary.qzv: Summarized parameter and (if enabled) feature selection information for the trained estimator. +:accuracy_results.qzv: Accuracy results visualization. +:probabilities.qza: Predicted class probabilities for each input sample. +:heatmap.qzv: A heatmap of the top 50 most important features from the table. +:training_targets.qza: Series containing true target values of train samples +:test_targets.qza: Series containing true target values of test samples + +| + +Description: +------------ +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 + + +| + + + + 10.21105/joss.00934 + @article{cite2, + author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard}, + journal = {Journal of machine learning research}, + number = {Oct}, + pages = {2825--2830}, + title = {Scikit-learn: Machine learning in Python}, + volume = {12}, + year = {2011} +} + + 10.1038/s41587-019-0209-9 + +
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