Auto feature engineering

The main bottleneck of automated feature engineering methods is the evaluation process which needs to train the ML model from sketch

Moreover, in practice, the computational resource is always limited

In this experiment, in order to verify the efficiency of EAAFE, we limit the total number of evaluations to 5000, and compare EAAFE with NFS

Automated feature engineering aims to help the data scientist by automatically creating many candidate features out of a dataset from which the best can be selected and used for training

In this article, we will walk through an example of using automated feature engineering with the featuretools Python library

“Feature engineering is the art part of data science” – Sergey Yurgenson Feature engineering is the process of preparing a dataset for machine learning by changing features or deriving new features to improve machine learning model performance

For example, suppose a lender wants to predict which loans will go bad