In the context of logistic regression, which estimation technique is primarily used to maximize predictive accuracy while ensuring that the model effectively predicts binary outcomes?

Maximum Likelihood Estimation, or MLE, looks for the parameter values that make the observed data most probable. Other options are incorrect because Ordinary Least Squares assumes a continuous outcome and normal errors, which is not true for 0/1 data; Ridge Regression adds a penalty to shrink coefficients, helping with overfitting, but it does not directly maximize the probability of the observed outcomes.