Figure 1
Machine intelligence learning optimizer: the MILO auto-machine learning (ML) infrastructure consists of begins with two datasets: (a) balanced data (Data Set 1) set used for training and validation, and (b) an unbalanced dataset (Data Set 2) for generalization. MILO removes missing values, assessed and scaled by the software. Unsupervised ML is then used for feature selection and engineering. The generated models are trained and then tested with the Data Set 1 during the supervised ML stage. Primary validation is then performed using Data Set 1 and followed by generalization using Data Set 2. Selected models can then be deployed thereafter as predictive model markup language (PMML) files.
