The correct answer is: A. Supervised Learning
Naive Bayes classifiers are a type of supervised learning algorithm. This means that they are trained on a set of data that includes both the features of the data and the labels that correspond to those features. The goal of the algorithm is to learn a model that can be used to predict the labels of new data points.
Supervised learning algorithms are often used in classification tasks, where the goal is to assign a label to each data point. For example, a supervised learning algorithm could be used to classify images of cats and dogs. The algorithm would be trained on a set of images that have already been labeled as cats or dogs. The goal of the algorithm would be to learn a model that can be used to predict the label of a new image.
Naive Bayes classifiers are a type of probabilistic classifier. This means that they assign a probability to each possible label for each data point. The probability of a label is determined by the features of the data point and the model that the algorithm has learned.
Naive Bayes classifiers are often used in spam filtering and email classification. They are also used in natural language processing tasks, such as part-of-speech tagging and named entity recognition.
Here is a brief explanation of each option:
- Supervised Learning is a type of machine learning in which the algorithm is trained on a set of data that includes both the features of the data and the labels that correspond to those features. The goal of the algorithm is to learn a model that can be used to predict the labels of new data points.
- Unsupervised Learning is a type of machine learning in which the algorithm is trained on a set of data that does not include any labels. The goal of the algorithm is to learn a model that can be used to find patterns in the data.
- Both is not a valid option.
- None is not a valid option.