A Discriminative Model is primarily used for?

A Discriminative Model is primarily used for mapping input features to class labels, which involves modeling the boundary between different classes in a dataset. This type of model focuses on learning how to differentiate between classes based on the input data it receives. It directly models the conditional probability of the class label given the input features, enabling it to make precise predictions about which class a given input belongs to. This characteristic is fundamental in various classification tasks, such as image recognition or spam detection, where the objective is to assign labels to data points based on learned features.

In contrast to other options, predicting future data trends relates more to time series analysis or regression models, which are not the focus of discriminative models. Generating synthetic data typically involves generative models, which aim to learn the joint probability distribution of the input features and labels, producing new sample data that resembles the training data. Enhancing data security does not directly pertain to the function of discriminative models; rather, it's often associated with encryption or access control methods. Thus, the primary application of discriminative models lies in their ability to effectively map inputs to corresponding class labels, making them essential tools in supervised machine learning.