Process / pipelineclassification-prediction

Logistic Regression

Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.

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Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI: 10.1111/j.2517-6161.1958.tb00292.x ↗
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). John Wiley & Sons. DOI: 10.1002/9781118548387 ↗

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Active Learning Logistic Regression AdaBoost ARFIMA Model Bayesian Case-Control Study Bayesian Dose-Response Analysis Bayesian k-nearest neighbors Bayesian Logistic Regression Bayesian Probit model Bayesian Statistical Inference Beneish M-Score Beta Regression Bradley-Terry Model CatBoost Causal Mediation Analysis Chi-square goodness-of-fit test Counterfactual Explanations Cox proportional hazards Cramer's V Credit Risk Models Credit Scoring Cross-tabulation analysis Decision Tree Discriminant Analysis Dose-Response Design Doubly Robust Estimation Elastic Net Explainable Decision Tree Explainable Naive Bayes Fairness-Aware ML Gamma Regression Generalized Linear Model Gradient Boosting Graph Attention Network Heckman Selection Model Hurdle Model Inverse Probability Weighting K-Nearest Neighbors Lasso Regression LightGBM Linear Discriminant Analysis Linear Discriminant Analysis (Classification)Maximum Likelihood Estimation Model Calibration Moderation Analysis Multi-layer Perceptron Multilayer Perceptron Multilevel Modeling Multinomial Logistic Regression Multinomial Logit Multiple Linear Regression Multiple Regression Analysis Multivariate Regression Naive Bayes Negative Binomial Regression Nonlinear Panel Data Analysis OLS Regression Ordered Logit Ordinal Logistic Regression Ordinal Regression Poisson Regression Probit Model Propensity Score Matching Proportion Test Random Forest Regularized Naive Bayes Ridge Regression Risk-adjusted case-control study Risk-adjusted Cox Proportional Hazards Risk-adjusted cross-sectional epidemiological study Risk-adjusted diagnostic accuracy study Risk-adjusted dose-response analysis Risk-adjusted screening test evaluation Robust Discriminant Analysis Robust Logistic Regression Robust Naive Bayes Robust Poisson Regression Robust Probit Model Semi-supervised Naive Bayes Semi-supervised Support Vector Machine SHAP Simple Linear Regression Stacking Stochastic Gradient Descent Support Vector Machine Survival Analysis Tobit Model Transformer XGBoost Zero-Inflated Poisson Regression

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