Evaluating XGBoost for balanced and Imbalanced datasets
The talk will introduce XGBoost, and provide examples of evaluation metrics for ML models in fraud and risk-scoring applications.
The talk will introduce XGBoost, and provide examples of evaluation metrics for ML models in fraud and risk-scoring applications.
The ESGentle team is throwing a big party this Saturday. They're inviting all environmental enthusiasts, climate tech entrepreneurs, AI experts, and investors to join them in the Earth Day celebration and explore the intersection of artificial intelligence and climate change.
The talk will introduce XGBoost and demonstrate how efficient machine learning models can automatically detect fraud cases depending on the data used.
In this talk, you will learn the theory behind GNNs, and look closely at the types of problems for which GNNs are well suited.
At dstack, our vision is to build an open-source platform for ML teams to train models and collaborate on data and models. We are currently seeking a backend engineer to join our team and assist in building the core of our platform.
You will learn about Ultralytics YOLOv8, how it works, how it compares to previous YOLO models and more.
On the webinar, we will talk about GNNs and the types of problems that GNNs are well-suited for. We will also discuss several approaches for modeling unstructured problems as classification or regression at various levels and more.
On the webinar, we will discuss what Ultralytics YOLOv8 is, its architecture, and training process, as well as the ways of using the Ultralytics YOLOv8 in real-world scenarios.