IBM Data Science Practice Test

Support Vector Machines (SVM) is a supervised learning algorithm that is primarily used for classification tasks, and sometimes for regression. In supervised learning, the model is trained on a labeled dataset, where the input data is paired with the correct output. SVM works by finding the hyperplane that best separates different classes in the data. It aims to maximize the margin between these classes, ensuring that the model generalizes well to unseen data.

SVM is widely regarded for its effectiveness in high-dimensional spaces and is also robust to overfitting, particularly in cases where the number of dimensions exceeds the number of samples. This makes it a popular choice for various machine learning tasks, such as text categorization and image recognition.

In contrast, clustering methods, such as k-means, do not require labeled outputs and are considered unsupervised learning techniques. Gaussian mixture models also fall into the realm of unsupervised learning, as they are used for modeling the distribution of data without predefined labels. Hidden Markov models, while they can be employed in certain supervised contexts, are fundamentally structured to identify probabilistic sequences, frequently utilized in applications involving time series data or the modeling of sequential information, making them less about direct classification tasks.

In summary, SVM is