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Feature importance for random forests #210
Description
When training a random forest, the scikit-learn package returns the feature importances (see http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), where the feature importance metric is the mean decrease impurity (as described here http://blog.datadive.net/selecting-good-features-part-iii-random-forests/). Can ML.Net also provide this information?
Activity
codemzs commentedon May 23, 2018
Hi @mjmckp , Yes, this information is maintained in the runtime but there isn't a high level API at the moment that exposes feature importance. We are working on exposing model summary via high level API that should contain feature importance.
zeahmed commentedon Jun 19, 2018
Looking at the new API proposal #371, this feature will be automatically available.
For example, in the proposed example look at this Line where predictor is created. The predictor contains several methods to save models/metadata and
SaveSummaryis one of them which emits feature importance where applicable.Also if the predictor implements
ICanGetSummaryInKeyValuePairsthen feature importance can be obtain as a List of KeyValuePairs. For example, see here for reference.shauheen commentedon Jun 28, 2018
Closing this issue as #371 would address the main problem.
Rev user input validation for new API (dotnet#210)
code migration from machinelearning-automl to machinelearning repo fe…
Rev user input validation for new API (dotnet#210)
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