The correct answer is A. PCA.
Supervised learning is a type of machine learning in which the model is trained on labeled data. This means that the model is given a set of data that includes both the input data and the desired output. The model then learns to map the input data to the output data.
PCA is a dimensionality reduction technique that is not supervised learning. PCA does not require labeled data. Instead, PCA is used to reduce the dimensionality of a dataset by finding a set of orthogonal axes that capture the most variance in the data.
Decision trees, naive Bayes, and linear regression are all supervised learning algorithms. Decision trees are a type of supervised learning algorithm that is used to classify or regress data. Naive Bayes is a type of supervised learning algorithm that is used to classify data. Linear regression is a type of supervised learning algorithm that is used to regress data.
In conclusion, the correct answer is A. PCA. PCA is a dimensionality reduction technique that is not supervised learning.