Choose whether true or false:  Decision tree cannot be used for clustering(1 Point)TrueFalse

Analysis of the Statement

1. Understanding Decision Trees: Decision trees are a supervised machine learning technique primarily used for classification and regression tasks. They function by splitting data into subsets based on feature values, ultimately leading to a prediction about the target variable.

2. Understanding Clustering: Clustering is an unsupervised learning method used to group similar data points together without prior labels. It aims to find natural groupings in data.

Explanation

1. Decision trees are not designed for clustering because they require labeled data to build a model. In contrast, clustering algorithms like K-means, hierarchical clustering, and DBSCAN operate on unlabelled data to identify inherent structures.

2. While you can use decision trees on data that has already been clustered to make predictions or analyze features, the primary use of decision trees does not include clustering.

Conclusion

Given that decision trees are inherently a supervised learning method and clustering is unsupervised, we can conclude:

Final Answer: True, decision trees cannot be used for clustering.