The bias-variance trade-off is a fundamental concept in machine learning and statistics that refers to the trade-off between a model’s ability to fit the training data well (low bias) and its ability to generalise to unseen data (low variance).
When developing and deploying machine learning models, it’s important that we split the dataset into ‘train’, ‘validation’, and ‘test’ datasets. This protects against an overfitted model, and helps ensure results are generalised. In this blog post we will look in to how to split the data, and why.
In Data Science, it’s pretty rare to come across a perfect dataset; data will frequently be of poor quality. It could have been recorded incorrectly, or it can be missing completely, which can be caused by poor data collection or storage methodologies.
In this post we discuss the importance of data scaling and common methods used to scale data.
Digital twins are a digital representation of a process, service or physical object. This computerised replica of the system or object is used to predict and assess performance and behaviour. A Digital twin’s main aim is to aid decision making by providing realistic indications of a systems current and future behaviour.
Have you been wondering how you can use the Industrial Internet of Things (IIoT) to get ISO 50001 certified? Or maybe how IIoT can help to identify Significant Energy Uses throughout your Energy Portfolio? This article will answer these questions, so you can reliably improve your sustainability credentials and cut your business costs.
Shift patterns and shift management plays a huge role in the energy efficiency and productivity of industrial assets. Providing insight into asset shifts is the key to discovering system inefficiencies and unlocking control strategies which can simplify operation and ensure production targets are met.
Creating an energy baseline is a game-changer when it comes to the analysis of changes to your assets, systems or sites. With an energy baseline, you’ll be able to monitor the effectiveness of a change by comparing the data before and after the change is made. This article is all about how to get an energy baseline set up.
Vibration analysis is one of the most important and effective methods of detecting the health of machinery. Vibration data can help us identify faults or detect warning signs of potential failures. It can also aid in the detection of misalignment or unbalance of assets such as bearings and rotating pieces of equipment. By looking at vibration data, we can also identify high and damaging levels of vibration.
All industrial facilities carry out maintenance. Two common types are preventative and predictive, and knowing the difference between the two and understanding which is best for your organisation is key.
This week is Water Saving Week! Waterwise, the leading UK non-profit organisation promoting water efficiency, organise this annual event aiming to raise awareness of issues surrounding water consumption.
Use this step-by-step checklist to understand the route to achieving the A grade in efficiency, with an energy management IoT solution.
Stay up to date with the latest industry news, platform developments and more.
