… Thus, organizations face both new opportunities and challenges, one of them is predictive
… is making use of machine learning algorithms for the design of predictive models in the …

… Machine Learning The origins of machine learning Uses and abuses of machine learning
Machine learning … The limits of machine learning Machine learning ethics How machines learn …

… Predictive modeling can also enhance our knowledge about attributes significant for outcome
prediction (this capability is algorithm-dependent). For instance, Delen et al. applied a Cox …

… assessment in terms of the predictive power of the data, and … experienced problems in
predictive modeling and analysis of … the basic methodologies of predictive modeling and analysis. …

… for machine learning. In this paper, we focus on machine learning predictive methods and
models… These include random forest, support vector machines, and other methods listed in …

… machine learning techniques and features to create novel type 2 diabetes predictive models…
background on the techniques used to create predictive models. “Methods” section presents …

… applications of machine learning (ML) predictive models in the … Nowadays, predictive models
are frequently applied in the … -the-art approaches that encompass ML models in the primary …

… predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the
predictive model for … , we built the predictive model and the model achieved good prediction …

… models predicting low and high levels of math performance and Vietnamese language, using
machine-learning … The models achieved very high accuracy (95–100%). A strong common …

… The ultimate goal is to offer predictive capability based on the knowledge gained from …
and machine learning (FIML) is demonstrated in a predictive setting to account for model form …