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Top Automated Feature Engineering Frameworks in Python in 2022
Introduction
The act of taking raw data and transforming it into features that are used to develop a predictive model using machine learning is referred to as feature engineering. It uses the raw data as its starting point.
The purpose of feature engineering is to improve the overall performance of machine learning models as well as to produce an input data set that is optimally suited for the algorithm that is being used for machine learning.
Data scientists can benefit from feature engineering since it can speed up the time it takes to extract variables from data, which in turn makes it possible to extract a greater number of variables. When businesses and data scientists automate the process of feature engineering, the resulting models will have a higher degree of precision.
Automated Feature Engineering
Feature engineering is almost always carried out manually, with a reliance on prior domain knowledge, intuitive judgement, and the manipulation of data. This procedure can be quite time-consuming, and the end result will have traits that are constrained by human subjectivity as well as the passage of time. The objective of automated feature engineering is to assist the data scientist by automatically…
