Points of Significance: Logistic regression

By Jake Lever, Martin Krzywinski, Naomi Altman. Nature Methods, 2016

1,952 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/356daeacafec3fd26bb203d5e014add99f877244

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Tianjian Chen
2. K. Plataniotis
3. Jahna Otterbacher
4. Yang Liu
5. Xinwei Zhang

In Statistics and Probability

1. Tianjian Chen
2. K. Plataniotis
3. Han Yu
4. Jahna Otterbacher
5. Yang Liu

Citations in the corpus

• Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention (2020, methodology): “We pick the following approaches as the prediction classifier: • Logistic Regression [ Kleinbaum et al. , 2002 ] • Random Forest [ Liaw et al. , 2002 ] • XGBoost [ Chen and Guestrin, 2016 ] • Neural Networks [ LeCun et al. , 2015 ] The computing platform for AI is supported by our Tencent Cloud…”
• A Comprehensive Survey of Loss Functions in Machine Learning (2020, neutral): “For the details, you can refer to [36].”
• Social Cues, Social Biases: Stereotypes in Annotations on People Images (2018, methodology): “In the case of a significant coefficient, we provide the odds ratio as a measure of effect size (Hilbe 2011).”
• Deep learning and machine vision for food processing: A survey (2021, methodology): “Logistic Regression is a machine learning method used to solve binary classification (0 or 1) problems and to estimate the probability of a certain scenario (Kleinbaum et al., 2002).”