Overview
For years, enhancing data features has been a common aim in data analytics, playing a critical role in supporting successful AI initiatives. However, before realizing the value of AI, feature engineering is essential. This white paper illustrates a few of powerful tools Minitab Statistical Software provides to help prepare data.
Feature engineering is the task of using knowledge about a process and its resulting data to extract properties, or features, that make predictive models work. Essentially, predictive models require input data to make predictions about the output of a process. The features or inputs you select to build a predictive model will have a major impact on the accuracy of the model’s predictions. To best apply feature engineering, knowledge of your process as well as your data are key to selecting features that will help you build accurate predictive models. The features you select should represent the underlying problem that you are trying to solve, making expertise in the process itself critical. At Minitab, we help practitioners like process experts, data scientists and business analysts leverage process knowledge to find data-driven solutions to solve their toughest business challenges.
In this whitepaper, we highlight seven feature engineering techniques and how you can successfully apply them using Minitab Statistical Software.
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