Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Abstract: The use machine learning-assisted optimization methods in the design of antennas have been increasing. Although neural networks (NNs) and Gaussian process regression (GPR) are widely used, ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. Trump pulls US out of more than 30 UN bodies Lack of oil ...

If it seems like Christmas trees are popping up earlier and earlier, that's because they are. According to the Minnesota Christmas Tree Association, 50% of their farms open before Thanksgiving, which ...

This research project conducts a comprehensive comparative study of Random Forest and Gradient-Boosted Trees (XGBoost, CatBoost, and LightGBM) for predicting Indonesian public university tuition fees ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

In an increasingly digital environment where data and advanced analytics challenge traditional economic modeling, the Bank of England is applying a fusion of machine learning (ML) with economic theory ...

Background: This study aims to develop a machine learning model to predict the 30-day mortality risk of hospitalized COVID-19 patients while leveraging federated learning to enhance data privacy and ...

Abstract: Gradient boosted decision tree algorithms only make it possible to interpolate data. Therefore, the prediction quality degrades if one of the features, such as time, lies outside the ...