The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

Conspectus The ongoing revolution of the natural sciences by the advent of machine
learning and artificial intelligence sparked significant interest in the material science …

The process of developing predictive models includes many stages. Most resources focus
on the modeling algorithms but neglect other critical aspects of the modeling process. This …

Cross-validation is one of the most widely used data resampling methods for model
selection and evaluation. Cross-validation can be used to tune the hyperparameters of …

Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

Feature selection (FS) is an important data processing method in pattern recognition and
data mining. Due to not considering characteristics of the FS problem itself, traditional …

Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …

The advent of high throughput technologies has led to a wealth of publicly available 'omics
data coming from different sources, such as transcriptomics, proteomics, metabolomics …

Feature selection is an important task in data mining and machine learning to reduce the
dimensionality of the data and increase the performance of an algorithm, such as a …

Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …