2.2. Probabilistic and Search Based Methods Flashcards
What is a Bayesian classifier?
A probabilistic model that applies Bayes’ theorem for classification tasks.
What are probabilistic methods in machine learning?
Techniques that use probability distributions to model uncertainty in data.
How does the Naive Bayes classifier work?
It assumes independence among features and calculates the probability of each class.
What is the role of prior probability in Bayesian methods?
It represents the initial belief about the probability of a class before observing the data.
What is a decision tree?
A search-based method that splits data into subsets based on feature values to make predictions.
What is the significance of likelihood in probabilistic models?
It measures how well the model explains the observed data given a specific class.
What is a search-based method in machine learning?
Techniques that explore the solution space to find optimal or near-optimal solutions.
How do genetic algorithms function as search-based methods?
They use evolutionary principles to optimize solutions through selection, crossover, and mutation.
What is the purpose of heuristic search methods?
To find solutions more efficiently by using rules of thumb or educated guesses.
What is the A* algorithm?
A search algorithm that finds the shortest path by combining cost and heuristic estimates.
How do probabilistic graphical models represent data?
They use graphs to depict the relationships between random variables and their conditional dependencies.
What is the Markov Chain Monte Carlo (MCMC) method?
A probabilistic method used for sampling from complex distributions.
How do search-based methods handle large solution spaces?
They use strategies like pruning and optimization techniques to reduce the search area.
What is the role of the likelihood function in probabilistic models?
It quantifies how well a model explains the observed data.
What are the applications of probabilistic and search-based methods?
They are used in natural language processing, computer vision, and optimization problems.
