Description: Feature transformation is a fundamental process in the field of machine learning and explainable artificial intelligence. It involves modifying the format or structure of the features in a dataset with the aim of improving the performance of a predictive model. This process can include various techniques such as normalization, standardization, encoding categorical variables, and creating new features from existing ones. By transforming features, the goal is to make it easier for the model to identify patterns and relationships in the data, which can lead to greater accuracy and effectiveness in predictions. Additionally, feature transformation is crucial for ensuring that models are interpretable and understandable, an essential aspect of explainable AI, where results need to be transparent and justified. In the context of AutoML (automated machine learning), feature transformation is performed automatically, allowing users without technical expertise to obtain high-quality models without needing deep knowledge of data manipulation. In summary, feature transformation is a critical step in the modeling process that can significantly influence the success of artificial intelligence projects.
