In the second part of this series, we explored how the BigML Node-RED bindings work in more detail and introduced the key concepts of input-output matching and node reification which will allow you to create more complex flows. In this third and final part of this introductory series, we are going to review what we know about inputs and outputs in a more systematic way, to introduce debugging facilities, and present an advanced type of node that allows you to inject WhizzML code directly into your flows. Each BigML node has a varying number of inputs and outputs, which are embedded in the message payload that Node-RED propagates across nodes. For example, the ensemble node has one input called dataset and one output called ensemble. You can change the input and output port labels when you need to connect two nodes whose inputs and outputs do not match. Say for example that a node has an output port label generically named resource and that you want to use that output value in a downstream node that requires a dataset input.

Real world Machine Learning is not just about the application of an algorithm to a dataset but a workflow, which involves a sequence of steps such as adding new features, sampling, removing anomalies, applying a few algorithms in cascade, and stacking a few others. The exact steps are often arrived at during iterative experiments performed by the practitioner. In other words, when it comes to the real life Machine Learning process, not everything is as automatic as various business media may make you believe. Usually, one starts by playing around a bit with the data to assess its quality and to get more familiar with it. Then, a significant amount of time is spent in feature engineering datasets, configuring models, evaluating them, and iterating or combining resources to improve results.