For which type of target is the weight estimation process designed to maximize the log-likelihood function?

The weight estimation process is tailored to maximize the log-likelihood function specifically for binary targets. This approach is particularly relevant in scenarios where the outcome is a dichotomy, such as success/failure or yes/no. The log-likelihood function, in this case, measures how well the model explains the observed data given the binary nature of the target. By maximizing the log-likelihood, the model effectively estimates the probabilities associated with the two possible outcomes, leading to better predictive performance and a more robust understanding of the underlying patterns in the data associated with the binary target.

In contrast, other target types have different considerations. For example, interval targets deal with continuous numeric outcomes, while categorical targets involve multiple distinct categories, which would require different modeling techniques that do not focus on maximizing the log-likelihood in the same way as binary targets. Primary targets may also not specifically relate to log-likelihood maximization but can encompass various types that include both binary and categorical data types. Hence, the focus on maximizing the log-likelihood function is most relevant for binary targets.