Probabilistic Classifier
A probabilistic classifier is a type of classifier algorithm that makes predictions based on the probability of an input belonging to a certain class. It uses statistical methods to calculate the likelihood of an input belonging to each class and then selects the class with the highest probability as the predicted output. Probabilistic classifiers are commonly used in machine learning and data science applications, such as image recognition, natural language processing, and fraud detection. Examples of probabilistic classifiers include Naive Bayes, Logistic Regression, and Random Forest. These classifiers are trained on labeled data to learn the probability distribution of each class and then use this information to make predictions on new, unseen data. Probabilistic classifiers are often preferred over other types of classifiers because they provide a measure of uncertainty in their predictions, which can be useful in decision-making processes.
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