Feature Preprocessor in Automated Machine Learning

The performance of an automated machine learning(AutoML) workflow depends on how we process and feed different types of variables to the model, due to most machine learning models only accept numerical variables. Thus, categorical features encoding becomes a necessary step for any automated machine learning approaches. It not only elevates the model quality but also helps in better feature engineering. There are two major feature reduction strategies: principal component analysis(PCA) and feature selection. PCA is widely used in current AutoML frameworks, due to it often used for reducing the dimensionality of a large dataset so that it becomes more practical to apply machine learning where the original data are inherently high dimensional.