Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and
regression tasks in Big Data. Researchers should be familiar with the strengths and …

In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

We sought to verify the reliability of machine learning (ML) in develo** diabetes prediction
models by utilizing big data. To this end, we compared the reliability of gradient boosting …

Class imbalance is an important consideration for cybersecurity and machine learning. We
explore classification performance in detecting web attacks in the recent CSE-CIC-IDS2018 …

Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF,
LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin …

Research into machine learning methods for fraud detection is of paramount importance,
largely due to the substantial financial implications associated with fraudulent activities. Our …

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on
different databases about the molecular and environmental origins of disease, into analytic …

LightGBM is a popular Gradient Boosted Decision Tree implementation for classification and
regression tasks. Our contribution is to answer a research question regarding LightGBM. We …

An innovative strategy for organizations to obtain value from their large datasets, allowing
them to guide future strategic actions and improve their initiatives, is the use of machine …

Introduction Unscheduled machine downtime can cause treatment interruptions and
adversely impact patient treatment outcomes. Conventional Quality Assurance (QA) …