Hi All, I am able to run LinearRegressionWithSGD on a small sample dataset (~60MB Libsvm file of sparse data) with 6700 features. val model = LinearRegressionWithSGD.train(examples, numIterations) At the end I get a model that model.weights.sizeres6: Int = 6699 I am assuming each entry in the model is weight for the corresponding feature/index. However,, if I want to get the top10 most important features or all features with weights higher than certain threshold, is that functionality available out-of-box? I can implement that on my own, but seems like a common feature that most of the people will need when they are working on high-dimensional dataset.
