Logistic regression

In statistics, logistic regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical.

Logistic regression was developed by statistician David Cox in 1958 (although much work was done in the single independent variable case almost two decades earlier). The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). As such it is not a classification method. It could be called a qualitative response/discrete choice model in the terminology of economics.

单词	Binary logit model
释义	Binary logit model 英语百科 Logistic regression In statistics, logistic regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. Logistic regression was developed by statistician David Cox in 1958 (although much work was done in the single independent variable case almost two decades earlier). The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). As such it is not a classification method. It could be called a qualitative response/discrete choice model in the terminology of economics.
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Binary logit model

Logistic regression