Unsupervised Machine Learning algorithms are used when the information used to train is neither classified nor labeled.TrueFalse

The statement is True.

Explanation:

Unsupervised machine learning refers to a class of algorithms that analyze and cluster unlabeled datasets. Unlike supervised learning, where the model is trained on a labeled dataset containing input-output pairs, unsupervised learning algorithms do not require labeled or classified information. Instead, they aim to discern patterns, structures, or groupings in the data without any prior knowledge of the outcomes.

Common applications of unsupervised learning include clustering, where similar data points are grouped together, and dimensionality reduction, which simplifies data by reducing the number of features while retaining essential characteristics. Algorithms such as K-means clustering, hierarchical clustering, and principal component analysis (PCA) exemplify this approach. This ability to work with raw, unmarked data is particularly useful in exploratory data analysis where the goal is to discover inherent structures or relationships within the dataset.