Elastic Net Regression
This tool fits a linear regressor with elastic-net regularization. This tool will then subsequently select the highest weighted OTUs or taxonomic groups using recursive feature elimination.
Last updated
This tool fits a linear regressor with elastic-net regularization. This tool will then subsequently select the highest weighted OTUs or taxonomic groups using recursive feature elimination.
Last updated
Selecting OTUs or taxonomic groups ("OTU signature") that are important (more heavily weighted) when predicting a quantitative metadata variable
The taxonomic level to aggregate the OTUs at. The OTUs will be grouped together (by summing the OTU values) at the selected taxonomic level before the analysis is applied.
Create comparative sample groups based on categorical variables uploaded in the metadata file.
Optionally create a categorical variable from a quantitative variable by using the Quantile Range feature on the Projects home page.
Specify how many features to display in the output. Leave blank to not do any filtering.
The type of loss to use when training the model.
Indicate whether the training set should remain the same every time a parameter is changed and the model is retrained.
Define the proportion of the data that should be randomly picked to form a training dataset.
You can set this value to be 1.0 if you don't plan on evaluating with a test dataset.
L1 (LASSO) regularization helps encourage sparsity within the selected features, which means that fewer features will be used to predict the experimental variable.
0.5 is recommended.
The maximum number of passes through the data during training.
Link back to boxplots
Save Snapshot: Save the results to the experiment notebook
Download: Downloads the results as a CSV file
Share: Creates a shareable link that allows you to share the results with others
