Tech Xplore

Ferroelectric memory enables one chip to sample randomness and compute for generative AI

For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...
Frontiers

From mother to infant: predicting infant temperament using maternal mental health measures and tabular machine learning models

Negative emotionality is a core dimension of infant temperament, characterized by heightened distress, reactivity, and difficulty with self-regulation. It has been consistently associated with later ...
IEEE

Shape Penalized Decision Forests for Imbalanced Data Classification

Abstract: Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional ...
Investopedia

Population: Definition in Statistics and How to Measure It

Osi Momoh is an expert on corporate finance and accounting, bonds, trading, cryptocurrency, and much more. Osi has 10+ years of experience in the investment industry, having served as a client-facing ...
C&EN

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.
Journal of Medical Internet Research

Sample Size Requirements for Popular Classification Algorithms in Tabular Clinical Data: Empirical Study

Objective: This study aimed to determine optimal sample sizes and the relationships between sample size and dataset-level characteristics over a variety of binary classification algorithms. Methods: A ...