Abstract: Feature selection in a traditional binary classification algorithm is always used in the stage of dataset preprocessing, which makes the obtained features not necessarily the best ones for ...

Abstract: In this paper, we propose a new clustering-based binary-class classification framework that integrates the clustering technique into a binary-class classification approach to handle the ...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

JSON Prompting is a technique for structuring instructions to AI models using the JavaScript Object Notation (JSON) format, making prompts clear, explicit, and machine-readable. Unlike traditional ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...

This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary classification tasks using linearly, non-linearly, and marginally separable datasets from the ...