When building machine learning or artificial intelligence models, it’s easy to get started but sometimes your model performs badly and it is hard to understand why. In this guide, we will be looking at how to improve your models.
While some of the tips will generalise to other types or areas of the field, our focus today will be on how to improve supervised learning models built for time-series data. Especially ones built for edge devices (tinyML), where inference or computation time (the time it takes for the embedded device to make one pass through the model) and memory footprint must be restricted to the devices capabilities.
There are two pathways to improving machine learning (ML) or artificial intelligence (AI) models. The first is data quantity and quality. This revolves around the data you have, how you got it and how much of it you got. The second is data processing, this involves getting a better understanding of your data and using that to help the model make the most out of the data you have.
In this first section we will focus on the data quantity and quality. Looking into different things that need to be taken into account in order to maximise the performance.
You can find a breakdown at the end of the guide of everything covered. In this guide we will look at the following topics which we believe are great ways to make the most out of your model and data:
Data Quantity & Quality
Quantity and Quality of Data
Labelling
Class Weights
Data Processing
Understanding the Data
Processing the Data
Sliding Window
Advanced Pre-processor
Conclusion
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