The dsAutoMl action does it all. It will explore your data, generate features, select features, create models, and autotune the hyper-parameters of those models. This action includes the four policies we have seen in my first two blogs: explorationPolicy, screenPolicy, transformationPolicy, and selectionPolicy. Please review my previous blogs if you need a refresher on the data exploration and cleaning process or feature generation and selection process. The dsAutoMl action builds on our prior discussions through model generation and autotuning. A data scientist can choose to build several models such as decision trees, random forests, gradient boosting models, and neural networks. In addition, the data scientist can control which objective function to optimize for and the number of K-folds to use. The output of the dsAutoMl action includes information about the features generated, information on the model pipelines generated, and an analytic store file for generating the features with new data.
This is an area where several companies are investing a lot of money, trying to simplify the process of training models.