Hands-On Data Warehousing with Azure Data Factory

Train the model

This is the most important phase; in fact it is time to build and train our machine learning model. In this step, the machine learning begins to work with the definition of the model and the next training. The model starts to extract knowledge from the large amounts of data that we had available, and nothing has been explained so far.

Let's now split the data for the training and the test model. Training and testing the model forms the basis for further usage of the model in predictive analytics. Given a dataset of 699 rows of data, which includes the predictor and response variables, we split the dataset into a convenient ratio (say 70:30) and allocate 490 rows for training and 209 rows for testing. The rows are selected ...