A UC Berkeley team used Apache Spark ML to predict airline delays at scale, training models on millions of flight records and ...

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: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...

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 rely on ...

A complete implementation of Logistic Regression with Gradient Descent optimization from scratch using only NumPy, demonstrating mathematical foundations of binary classification for diabetes ...

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 = ...

This cross-sectional study investigates the interplay of lifestyle, behavioral, and psychosocial factors in predicting depressive symptoms among Chinese college students (N=508) using binary logistic ...

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 ...