IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

The algorithm that is commonly used in supervised machine learning is Decision Trees. This method is well-suited for tasks where the goal is to predict an output variable based on one or more input features. Decision Trees work by splitting the data into subsets based on the value of input variables, creating a tree-like structure that leads to predictive outcomes.

In supervised learning, we have labeled data—meaning each training example comes with a corresponding output label. Decision Trees utilize this labeled data effectively, enabling them to make predictions on unseen data by following the decision logic established during training.

In contrast, K-Means clustering and Isolation Forest are primarily associated with unsupervised learning. K-Means is used for grouping data into k clusters based solely on input features without supervision, while Isolation Forest is an anomaly detection technique that doesn't require labeled outputs. Principal Component Analysis also falls under unsupervised learning; it’s used for reducing the dimensionality of data rather than for prediction tasks based on labeled data. Hence, Decision Trees stand out as a quintessential example of a supervised learning algorithm.