The correct answer is A. To perform text classification tasks based on conditional probabilities.
Naive Bayes is a supervised machine learning algorithm that is used for classification tasks. It is based on Bayes’ theorem, which is a mathematical formula that calculates the probability of an event occurring given the occurrence of other events. Naive Bayes assumes that the features of a data set are independent of each other, which is why it is called “naive.” However, this assumption is often not true in real-world data sets. Despite this limitation, Naive Bayes is a simple and effective algorithm that is often used for text classification tasks.
Option B is incorrect because dimensionality reduction is a technique that is used to reduce the number of features in a data set. This can be done by finding a set of features that are highly correlated with the target variable.
Option C is incorrect because Naive Bayes is not used to create regression models. Regression models are used to predict continuous values, while Naive Bayes is used to predict discrete values.
Option D is incorrect because Naive Bayes is not used to calculate p-values. P-values are used to measure the statistical significance of a result.