The extremely randomized trees classifier (scikitlearn) provides a (multivariate)feature importance measurementEnsemble methods/feature importance evaluation. For each feature, the classifier produces a statistical measurement (and the corresponding standard deviation) for how important the feature was for predicting the target variable. The basic use of this information is to create a "feature ranking" among the features (from high importance value to low). The question that I have is if I can use this information to make a conclusion that my features are weak or strong for my training data? Has the size of the "importance value" a meaning in itself? For instance, the highest ranking feature has an importance value of 0.0494 with a standard deviation of 0.024. Can I make the conclusion that the features are weak because the "importance values" are very small.

The extremely randomized trees classifier (scikitlearn) provides a (multivariate) feature importance measurement Ensemble methods/feature importance evaluation. For each feature, the classifier produces a statistical measurement (and the corresponding standard deviation) for how important the feature was for predicting the target variable. The basic use of this information is to create a "feature ranking" among the features (from high importance value to low). 

The question that I have is if I can use this information to make a conclusion that my features are weak or strong for my training data? Has the size of the "importance value" a meaning in itself? For instance, the highest ranking feature has an importance value of 0.0494 with a standard deviation of 0.024. Can I make the conclusion that the features are weak because the "importance values" are very small?

The extremely randomized trees classifier (scikitlearn) provides a (multivariate)feature importance measurementEnsemble methods/feature importance evaluation. For each feature, the classifier produces a statistical measurement (and the corresponding standard deviation) for how important the feature was for predicting the target variable. The basic use of this information is to create a "feature ranking" among the features (from high importance value to low). The question that I have is if I can use this information to make a conclusion that my features are weak or strong for my training data? Has the size of the "importance value" a meaning in itself? For instance, the highest ranking feature has an importance value of 0.00494 with a standard deviation of 0.024. Can I make the conclusion that the features are weak because the "importance values" are very small.

The extremely randomized trees classifier (scikitlearn) provides a (multivariate)feature importance measurementEnsemble methods/feature importance evaluation. For each feature, the classifier produces a statistical measurement (and the corresponding standard deviation) for how important the feature was for predicting the target variable. The basic use of this information is to create a "feature ranking" among the features (from high importance value to low). The question that I have is if I can use this information to make a conclusion that my features are weak or strong for my training data? Has the size of the "importance value" a meaning in itself? For instance, the highest ranking feature has an importance value of 0.0494 with a standard deviation of 0.024. Can I make the conclusion that the features are weak because the "importance values" are very small.