比较least squares, logit,probit model的maximised log-likelihood

Least squares, logit, and probit models are different types of regression models. Least squares is a linear regression model, while logit and probit are nonlinear regression models used for binary classification problems.

The maximized log-likelihood is a measure of how well a model fits the data. For least squares, the maximized log-likelihood is the negative sum of squared errors. For logit and probit, the maximized log-likelihood is the sum of the log-likelihood function evaluated at the estimated parameters.

Therefore, comparing the maximized log-likelihoods of these models is not straightforward as they have different forms. However, we can say that the logit and probit models are more appropriate for binary classification problems, while the least squares model is better suited for continuous response variables.