In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benefit, ideally both in terms of accuracy and interpretability at the same time. Often times it will be applied in combination with simple machine learning techniques such as regression models or decision trees to boost their performance (whilst maintaining the interpretability property which is so often needed in analytical modeling) but it may also improve complex techniques such as XGBoost and neural networks. Feature engineering aims at designing smart features in one of two possible ways: either by adjusting existing features using various transformations or by extracting or creating new meaningful features (a process often called “featurization”) from different sources (e.g., transactional data, network data, time series data, text data, etc.).

为了提高任何机器学习模型的性能，重要的是更多地关注数据本身，而不是不断开发新算法。这正是特征工程的目标。它可以被定义为巧妙的数据工程，从而利用机器学习技术的内在偏差为我们谋取利益，理想情况下同时在准确性和可解释性方面。通常，它会与简单的机器学习技术（例如回归模型或决策树）结合应用以提高其性能（同时保持分析建模中经常需要的可解释性属性），但它也可以改进复杂的技术，例如 XGBoost 和神经网络。特征工程旨在以两种可能的方式之一设计智能特征：