IBM Data Science Practice Test

Decision trees, support vector machines, and naive Bayes are techniques used to solve what kind of problem?

1. Regression

2. Clustering

3. Classification

4. Reinforcement

The correct answer is: Classification

Decision trees, support vector machines, and naive Bayes are all techniques primarily used for classification tasks. Classification involves predicting the category or class label of an observation based on its features, and these techniques provide different methodologies to achieve this. Decision trees use a tree-like model to split data into subsets based on feature values, ultimately classifying data points into distinct classes based on the learned patterns. Support vector machines create a hyperplane that best separates different classes in the feature space, making it highly effective for binary and multi-class classification tasks. Naive Bayes applies Bayes' theorem and assumes independence among predictors, utilizing probabilities to classify instances. While the other options represent important areas in machine learning—regression focuses on predicting continuous values, clustering involves grouping similar observations without predefined labels, and reinforcement learning pertains to learning optimal actions through trial and error—these specific techniques are not designed to address those types of problems. Therefore, classification is the correct context in which to categorize these methods.