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Random Forest Classifier
- Assess the performance of a random forest classifier on the OTU data in predicting a categorical variable
Machine learning models tend to work best with a dataset with a large number of samples
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.
Train the model using either by splitting the data into a training and test set or by cross-validating over a specified number of folds.
If set to yes, ensures that the same samples are used as the training set every the model is retrained. This useful to keep the test set untouched.
Define the proportion of the data that should be randomly picked to form a training dataset.
- Assess the predictive performance of your model using the test AUC (area under the ROC curve shown). Note: Whenever possible, it is still recommended to validate a trained model against an independent dataset (one that is collected outside of your study).
- Tune your model for better performance by looking only at the validation AUC. Tuning refers to changing the configurable parameters to try to achieve a better performance for your dataset. It is important to not tune against the test AUC to ensure you don't overfit your model to the test set.
- The AUC tells you the probability that a randomly sampled positive patient will have a higher predicted score for the positive class than the negative class. The AUC will be shown in a "one-vs-all" format.
- Hover over the ROC curve generated for the test data
- 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