Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One
application domain is data science. New techniques in automating the creation of AI, known …

We explore trust in a relatively new area of data science: Automated Machine Learning
(AutoML). In AutoML, AI methods are used to generate and optimize machine learning …

Feature engineering is a crucial step in the process of predictive modeling. It involves the
transformation of given feature space, typically using mathematical functions, with the …

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …

“AI as a Service”(AIaaS) is a rapidly growing market, offering various plug-and-play AI
services and tools. AIaaS enables its customers (users)—who may lack the expertise, data …

Feature transformation aims to generate new pattern-discriminative feature space from
original features to improve downstream machine learning (ML) task performances …

Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data
scientists and domain experts (eg, data exploration, model training, etc.). Only till recently …