Feature engineering

Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive. The need for manual feature engineering can be obviated by automated feature learning.Coming up with features is difficult, time-consuming, requires expert knowledge. 'Applied machine learning' is basically feature engineering.The algorithms we used are very standard for Kagglers. We spent most of our efforts in feature engineering. We were also very careful to discard features likely to expose us to the risk of over-fitting our model.…some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used. Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive. The need for manual feature engineering can be obviated by automated feature learning.