I am working on
Spark MLlib and have a project where I have to make predictions for numeric data based on non-numeric features. I am a bit confused about which
regression algorithm to use from
Spark MLlib library primarily due to being new at this. The algorithms present in Spark MLlib library are:
-linear models (SVMs, logistic regression, linear regression) -naive Bayes -decision trees -ensembles of trees (Random Forests and Gradient-Boosted Trees) -isotonic regression
Can anyone provide me some guidance as to which algorithm will be suitable for predictions for numeric data based on non-numeric features?