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?

In logistic regression we use maximum‑likelihood estimation to find the parameter values that make the observed binary outcomes most probable. By maximizing the likelihood function, the fitted model assigns probabilities that best match the actual 0/1 labels, thereby improving predictive accuracy. The likelihood is built from the Bernoulli distribution for each observation, and the optimization is usually done with gradient‑based algorithms. For example, if a data point is labeled 1, the likelihood contribution is the predicted probability p, while for a label 0 it is 1‑p; maximizing the product of these across all data points gives the best‑fitting parameters. This approach directly targets the binary prediction task, ensuring the model’s outputs align with the true outcomes.