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This paper discusses the critical decision process of extracting or selectingthe features in a supervised learning context. It is often confusing to find asuitable method to reduce dimensionality. There are pros and cons to decidingbetween a feature selection and feature extraction according to the data'snature and the user's preferences. Indeed, the user may want to emphasize theresults toward integrity or interpretability and a specific data resolution.This paper proposes a new method to choose the best dimensionality reductionmethod in a supervised learning context. It also helps to drop or reconstructthe features until a target resolution is reached. This target resolution canbe user-defined, or it can be automatically defined by the method. The methodapplies a regression or a classification, evaluates the results, and gives adiagnosis about the best dimensionality reduction process in this specificsupervised learning context. The main algorithms used are the Random Forestalgorithms (RF), the Principal Component Analysis (PCA) algorithm, and themultilayer perceptron (MLP) neural network algorithm. Six use cases arepresented, and every one is based on some well-known technique to generatesynthetic data. This research discusses each choice that can be made in theprocess, aiming to clarify the issues about the entire decision process ofselecting or extracting the features.

DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction