The correct answer is D. Naive Bayes.
Naive Bayes is a simple probabilistic machine learning algorithm that is often used for classification. It is based on Bayes’ theorem, which is a mathematical formula that relates the probability of two events to the probability of one event given the other.
Naive Bayes assumes that the features of a data set are independent of each other. This means that the probability of a class label given a set of features can be calculated by multiplying the probabilities of each feature occurring given the class label.
Naive Bayes is a relatively simple algorithm to understand and implement. It is also relatively fast to train and can be used to classify data sets with a large number of features. However, naive Bayes can be inaccurate if the features of a data set are not independent.
K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Decision trees are a type of supervised learning algorithm that can be used for both classification and regression tasks. Decision trees are built by recursively splitting the data set into smaller and smaller subsets until each subset contains only observations of a single class. The splitting is done based on a set of rules that are learned from the data.
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (i.e., uncorrelated with) the preceding components.
In conclusion, the correct answer is D. Naive Bayes.