Which machine learning model focuses primarily on classification tasks?

Support Vector Machines (SVM) are specifically designed for classification tasks and play a significant role in supervised learning. The main objective of an SVM is to find the optimal hyperplane that divides a dataset into classes with the maximum margin. It operates by identifying the support vectors, which are the data points that are closest to the hyperplane, and making predictions based on the position of new data points relative to this hyperplane.

While other models listed, such as neural networks and deep learning, can also perform classification tasks, they are not limited to this purpose. Neural networks are versatile and can be applied to both classification and regression problems depending on their architecture and the output layer's configuration. Deep learning, a subset of neural networks, extends this capability further by enabling the modeling of complex patterns in data but is not solely focused on classification tasks.

Generative models, on the other hand, are primarily concerned with understanding and modeling the underlying distribution of the input data, which makes them useful for generating new data points rather than directly classifying them.

Thus, SVM stands out as a model that is explicitly tailored for classification, making it the most appropriate choice among the options provided.