Categorical Features and Missing Values

Hi mmlspark team,

Given I have a LightGBM model trained in python with a dataset that contains categorical features and missing values. Now LightGBM deals with both under the hood which is neat.

After saving the model I want to load this in the Scala implementation of mmlspark to make predictions,, which works fine. It gives me a LightGBMBooster. However, the scala implementation requires a Vector of Double values for the predict or predictLeaf method. This leaves me wonder how to deal with categorical features and missing values.

**Categorical Features:** Is the right approach here to run the categorical features of the train set through a StringIndexer and then use that indexer to transform my input features into the correct numerical representation?

**Missing Values:** Here I am a bit puzzled what the right approach is to represent missing values.

Many thanks
Seb