Which type of machine learning technique is best suited for supervised learning tasks? 

Decision trees are well-suited for supervised learning tasks, where the goal is to predict an output variable based on input features. The tree structure allows for easy interpretation of how the input variables are related to the output, making it ideal for classification and regression tasks. K-means Clustering: A clustering technique used in unsupervised learning. PCA: A dimensionality reduction technique, not a supervised learning algorithm. k-Nearest Neighbors (k-NN): While k-NN can be used for supervised learning, decision trees are generally more interpretable. Support Vector Machines (SVM): While SVMs are also used in supervised learning, decision trees offer more visual interpretability.