Automated feature engineering improves upon the traditional approach to feature engineering by automatically extracting useful and meaningful features from a set of related data tables with a framework that can be applied to any problem.
AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities.
To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features.
In recent years, the importance of feature engineering has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications.
In this paper, we develop the Data Science Machine, which is able to derive predictive models from raw data automatically.
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction.