Mastering machine learning with tunable capabilities for eliminating overfitting

Engineers and scientists are building smarter products and services, such as advanced driver assistance systems and predictive maintenance applications, driven by analytics based on industrial data. Analytics modeling is the ability to describe and predict a system’s behavior from historical data using domain-specific techniques for data preparation, feature engineering and machine learning. Combining these capabilities with automatic code generation, targeting edge-to-cloud, enables reuse while automating actions and decisions.

By leveraging the increased availability of ‘big industrial data’, compute power and scalable software tools, it becomes easier than ever to use machine learning in engineering applications. ML methods ‘learn’ information directly from the industrial data without relying on a predetermined equation as a model and are particularly suitable for today’s complex systems. However, two of the most common challenges faced by engineers and scientists who are modeling with machine learning relate to choosing a suitable ML model to classify their domain-specific data and eliminating data overfitting.