This is the feature engineering, training and evaluation experiment in the collection "Predictive Maintenance Modelling Guide". Created by a Microsoft Employee.
This experiment demonstrates the the feature engineering, training and evaluation steps of [Predictive Maintenance Modelling Guide R Notebook] using Azure Machine Learning Studio. The data sets from [Predictive Maintenance Modelling Guide Data Sets] experiment are used for feature engineering and labeling. The final labeled features are used to train and evaluate the model in Azure Machine Learning Studio. A time-dependent splitting is used for training and evaluation of the model at three different points in time. It is recommended that you first examine the data sets experiment of the collection and the R notebook to understand the steps before examining the flow in this experiment. : https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Implementation-Guide-1 : https://gallery.cortanaintelligence.com/Experiment/Predictive-Maintenance-Implementation-Guide-Data-Sets-1 : https://gallery.cortanaintelligence.com/Notebook/Predictive-Maintenance-Implementation-Guide-R-Notebook-2