The Methodology Behind Times Voter Trends

Read the detailed methodology behind the Times Voter Trends reports for 2026. By Nate Cohn See more of our coverage in your search results.Encuentra más de nuestra cobertura en los resultados de ...
GitHub

MBTI Personality Prediction Using NLP and Machine Learning

MBTI Personality Prediction using Natural Language Processing and Machine Learning. - Jay00066/Personality_Prediction_NLP_ML ...
BIOENGINEER

Machine Learning Flags Preterm Birth Risk Across Eleven African Countries

A machine learning study of 411,863 women across eleven Sub-Saharan African countries achieved 90.2 percent accuracy in ...
GitHub

Amazon Reviews NLP Pipeline

End-to-end Machine Learning pipeline on Amazon customer reviews: NLP feature extraction, time-series trend analysis, and neural network sentiment classification. - ewangila/Amazon_Reviews_NLP_Pipeline ...

[G-Certification Day 8] What is Supervised Learning in AI? Linear Regression, SVM, and Decision Trees

Hello everyone, stay safe!! This is Takachan the engineer.AI that identifies spam, AI that predicts sales, AI that ...
BIOENGINEER

Open-data machine learning framework reaches near-perfect breast cancer diagnosis on benchmark dataset

A reproducible open-data framework using the Wisconsin Diagnostic Breast Cancer dataset achieved near-ceiling discrimination ...

The Trade-off with Explainability: The Difference Between a '95% Accurate Model That Never Gets Adopted' and a '60% Accurate System Used Every Day'

A machine learning model has 95% accuracy, yet it sits idle month after month. Meanwhile, a simple scoring system using a decision tree with only 62% accuracy is used daily for sales decision-making.
PBS

Ghost in the Machine

Ghost in the Machine reveals AI’s troubled history and present-day impacts. Ghost in the Machine reveals how the human values, biases, and power structures behind artificial intelligence are shaping ...
Scientific Research Publishing

Dumitrescu, E., Hué, S., Hurlin, C., & Tokpavi, S. (2022). Machine Learning for Credit Scoring: Improving Logistic Regression with Non-Linear Decision-Tree Effects. European ...

ABSTRACT: This paper compares the performance of traditional models and machine learning (ML) models for mortgage credit risk prediction using the Freddie Mac Single-Family Loan-Level Dataset. A ...
Frontiers

Machine learning versus traditional regression models for predicting diabetic retinopathy screening adherence among community-dwelling older adults with diabetes

Diabetic retinopathy (DR) is a leading cause of preventable vision loss among patients with diabetes, yet screening adherence remains suboptimal. Existing studies have mainly focused on clinical or ...
Frontiers

A machine-learning-assisted logistic regression model for predicting post-operative delirium in older adults undergoing total knee arthroplasty

Post-operative delirium (POD) is a common complication in older adults undergoing total knee arthroplasty (TKA). Procedure-specific predictive models remain limited. This study aimed to develop a ...
databricks.com

Machine Learning Use Cases: Practical Industry Applications

Machine learning use cases now span every major industry — from fraud detection and demand forecasting to computer vision diagnostics and predictive maintenance — giving data teams a proven playbook ...