Final answer:
In logistic regression, the most likely class is chosen using Maximum likelihood estimation, which finds the model parameters that make the observed data the most likely. Cross-validation, mean squared error, and ridge regression are not directly related to choosing the most likely class for predictions in logistic regression.
Step-by-step explanation:
When predicting with logistic regression, the class is chosen based on the probability that is output by the logistic regression model. The most likely class is typically the one with the highest probability after the logistic function is applied to the linear combination of predictors. In this context, the option that aligns with choosing the most likely class is Maximum likelihood estimation (Option 1). This method involves choosing the model parameters that maximize the likelihood of the observed data. Unlike options such as mean squared error or ridge regression, which are more focused on minimizing predictive error or managing multicollinearity, maximum likelihood directly pertains to the likelihood of class membership given the data.
Cross-validation (Option 2) is a model evaluation method that is unrelated to the actual prediction process, while mean squared error (Option 3) refers to a measure of prediction accuracy for continuous outcomes, not applicable to categorical class assignments in logistic regression. Ridge regression (Option 4) is a type of regularization that mitigates multicollinearity but doesn't directly determine class membership in predictions.