Abstract: In highly imbalanced binary classification tasks with asymmetric misclassification costs, traditional cost-insensitive learning strategies fail to reflect true risk and often yield poor ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

Abstract: Leukemia, a blood cancer diagnosed primarily through microscopic examination of blood smears, poses challenges due to the cost, time, and labor-intensive nature of the process. This paper ...

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 ...

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

1 Department of Information Technology and Computer Science, School of Computing and Mathematics, The Cooperative University of Kenya, Nairobi, Kenya. 2 Department of Computing and Informatics, School ...

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 = ...

Instead of running Python scripts manually for routine tasks, why not automate them to run on their own, and at the time you want? Windows Task Scheduler lets you schedule tasks to run automatically ...

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 ...