GLM & count
23 methods in this family.
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Active Learning Logistic RegressionActive Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, aDIF AnalysisDifferential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability rEnsemble Logistic RegressionEnsemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by aveGamma RegressionGamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagGeneralized Linear ModelThe Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportionLogistic RegressionLogistic 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 p
All methods 23
Active Learning Logistic RegressionDIF AnalysisEnsemble Logistic RegressionGamma RegressionGeneralized Linear ModelLogistic RegressionLogistic regression (ML)Multinomial Logistic RegressionOnline Logistic RegressionOrdinal Logistic RegressionOrdinal RegressionRobust Generalized linear modelRobust Logistic RegressionRobust Multinomial Logistic RegressionRobust Negative Binomial RegressionRobust Poisson RegressionRobust Probit ModelRobust Zero-Inflated ModelSelf-supervised Logistic RegressionSemi-supervised Logistic RegressionZero-inflated modelZero-Inflated Negative Binomial RegressionZero-Inflated Poisson Regression