IoT data analytics and machine learning comparison and assessment
Machine learning algorithms have their place in IoT. The typical case is when there is a plethora of streaming data that needs to produce some meaningful conclusion. A small collection of sensors may only need a simple rules engine on the edge in a latency-sensitive application. Others may stream data to a cloud service and apply rules there for systems with less-aggressive latency demands.
When large amounts of data, unstructured data, and real-time analytics come into play, we need to consider the use of machine learning to solve some of the hardest problems.
In this section, we detail some tips and reminders in deploying machine learning analytics, and what use cases may warrant such tools.
Training phase:
- For a random forest, use bagging techniques to create ensembles.
- When using a random forest, ensure you maximize the number of decision trees.
- Watch overfitting. Overfitting...
