Clustering algorithms, decision trees, and random forests

Clustering algorithms are used for unsupervised learning tasks, which means they are used to find patterns in data without any predefined labels or categories. The goal of clustering algorithms is to group similar data points together in clusters, while keeping dissimilar data points separate.

There are several types of clustering algorithms, including K-means, hierarchical clustering, and density-based clustering. K-means is a popular clustering algorithm that works by dividing a dataset into K clusters, where K is a predefined number of clusters. Hierarchical clustering is another clustering algorithm that creates a hierarchy of clusters based on the similarity between data points. Density-based clustering algorithms, such as DBSCAN, group together data points that are closely packed together in high-density regions.

Decision trees, on the other hand, are used for supervised learning tasks, which means they are used...