In logistic regression, we assume a... (logit = logarithm of the odds)

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BlackTom AI · Accepted Answer

The question asks about the core assumption in logistic regression, given that logit = logarithm of the odds.
Option a: 'linear relationships between the logit of continuous features and the outcome variable.' This is not the standard formulation. Logistic regression models the logit as a linear function of the features, not the logit of the features themselves relating to the outcome variable directly.
Option b: 'linear relationship between continuous features and the logit of the outcome variable.' This matches the fundamental assumption: the log-odds (logit) are modeled as a linear combination of the predictors (the continuous features or covariates).
Option c: 'a nonlinear relationship between continuous features and the logit of the outcome variable.' This would violate the key linearity assumption in the logit; nonlinearities are handled by transformations or nonlinear models, not by the basic logistic model.
Option d: 'linear relationships between the logit of continuous features and the logit of the outcome variable.' This misstates the model: the logit is a linear function of the features, not a linear relationship between the logits of features and the logits of the outcome variable.
In summary, only option b correctly captures the standard logistic regression assumption that the logit of the outcome is a linear function of the continuous predictor features.

In logistic regression, we assume a... (logit = logarithm of the odds)

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