A characteristic or a set of characteristics can be considered as a feature in the act of machine learning. When these characteristics are transformed into some measurable form, they are labeled as features. Feature engineering is creating new input features from your existing ones. In general, the data cleaning process can be assumed as a subtraction process and the feature engineering as a process of addition. Feature engineering can directly be defined as the process of creating new features from the existing features in a dataset. 

Building machine learning models can often be a complex and boring process and involves many steps. So, B2Metric ML Studio is able to automate a certain percentage of feature engineering tasks, then the data scientists or the domain experts can focus on other aspects of the model. 

Automated feature engineering helps on time saving, building better predictive models, creating meaningful features and preventing leakage of datas. At the same time, the datas are prepared by B2Metric AI to automatically modeling, to execute operations like one-hot encoding, missing data imputation, text mining, standardization, and data partitioning.

If this has to be done by hand this would have taken several days, yet with automated machine learning, this only took hardly any hours. The key strength of automated feature engineering is when it’s applied to regrouping or reshaping data. This is why it is recommended that engaging the creativity and experience of business domain experts for domain-specific feature knowledge, such as how to correctly interpret the data.