title: LightGBM for feature selection
body: <p>I'm working on a binary classification problem, my training data has millions of records and ~2000 variables. I'm running lightGBM for feature selection and using the features selected from lightGBM to run Neural network (using Keras) model for predictions. I have couple of questions on the approach I'm following.</p>
  <ol>
  <li>I'm doing hyper-parameter tuning when using lightGBM for feature selection. This is based on my understanding that with the change in hyper-parameters, features selected will also be different. I'm using 'goss' algorithm and 'gain' as feature importance type. I have seen couple of articles where they are using lightGBM to do feature selection but I haven't seen any where doing hyper-parameter tuning they are just using default settings. Is this correct approach?</li>
  <li>Is it ok to use lightGBM for feature selection and Neural network to build the model for predictions based on the features selected from lightGBM?</li>
  </ol>
  <p>Any help is much appreciated. Thanks</p>