Automated machine learning: the new data science challenge

Abstract

The world is changing quite rapidly while increasingly tuning into digitalization. However, it is important to note that data science is what most technology is evolving around and data is definitely the future of everything. For industries, adopting a “data science approach” is no longer an option, it becomes an obligation in order to enhance their business rather than survive. This paper offers a roadmap for anyone interested in this research field or getting started with “machine learning” learning while enabling the reader to easily comprehend the key concepts behind. Indeed, it examines the benefits of automated machine learning systems, starting with defining machine learning vocabulary and basic concepts. Then, explaining how to, concretely, build up a machine learning model by highlighting the challenges related to data and algorithms. Finally, exposing a summary of two studies applying machine learning in two different fields, namely transportation for road traffic forecasting and supply chain management for demand prediction where the predictive performance of various models iscompared based on different metrics.

Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior

“…Machine learning pipelines are the collection of output from a series of machine learning processes started from data exploration and pre-processing, features engineering, algorithm selection, hyper-parameters configuration and tuning. Different with manual machine learning that manually executed by human, there are a number of computing mechanisms in automated machine learning that used search optimization for automating the processes mainly in selecting features of the models, identifying the suitable algorithms and hyper-parameters tuning [27 , 28] .…”

Machine learning-based electricity theft detection using support vector machines

“…MCC score ranges between -1 to 1, A value of 1 indicates an accurate prediction, a value of 0 indicates no class separation capability, and a value of -1 indicates an incorrect prediction. It is formulated in [24] using (7).…”

Optimizing Multi-Level Crop Disease Identification using Advanced Neural Architecture Search in Deep Transfer Learning

“…Agriculture plays a vital role in many economies, such as Morocco, but it faces risks from temperature changes, insects, and diseases. However, given the size of agricultural lands and the scarcity of trained workers, conventional disease detection techniques are frequently arbitrary and ineffective [1]. We are creating cutting-edge methods that use deep learning, image sensors, and computer vision to overcome these obstacles.…”