Suggest that adding classification to LSTM and regressor to RandomForest on these two most popular models.

RandomForest is an awesome classification tool but it lacks of sequential data maneuver. Put a classification onto RNN/LSTM/GRU would be a good choice to get output probabilities. Ryan mentioned using a softmax layer as last layer with nodes equals to the class number.

Also, on time ser

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

Unlike N-Gram, the current Skip-Gram tokenizer does not allow variable length tokens. This ticket is to implement a min max scheme similar to the N-Gram tokenizer such that each token is a k-skip-n-gram. In other words, instead of fi