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Machine learning fits mathematical models to data in order to derive insights or make predictions. These models take features as input. A feature is a numeric repre‐sentation of an aspect of raw data. Features sit between data and models in the machine learning pipeline.

Sep 9, 2015 · Throughout this chapter, we discuss how deep learning can contribute to these goals by stepping up ongoing research activities, improving the efficiency and speed of existing methods, and...

Apr 22, 2010 · Feature learning revisited Handcrafted features { Result from knowledge acquired by the feature designer. { This knowledge was acquired on multiple datasets associated with related tasks. Multilayer features { Trained on a single dataset (e.g. CNNs). { Requires lots of training data. { Interesting training data is expensive Multitask/multilayer ...

Machine learning provides you with extremely powerful tools for decision making ... ... but until a breakthrough in AI, the role of the developer's decision will still be crucial.

May 11, 2019 · With the huge amount of data available and the consequent requirements for Artificial Intelligence and good machine learning techniques, new problems arise and novel approaches to feature...

Beginning with the basic concepts and techniques of feature engineering, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series and images, with fully worked-out case studies.

Feature engineering is a critical aspect of machine learning that involves the design and selection of features to improve model performance. It includes tasks like feature combination, selection, and normalization to mitigate issues such as overfitting and model interpretability.

Feature engineering plays a critical role in the machine learning pipeline, profoundly impacting the performance of predictive models. This survey provides a comprehensive overview of the latest advancements in feature engineering, including its techniques, challenges, and best practices.

Through real-world case studies, we illustrate the diverse range of techniques employed in feature engineering and their implications on model performance. One of the keys focuses of this paper is on elucidating the process of selecting features in machine learning.