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Test your knowledge about different machine learning methods

What is the main characteristic of Decision Trees?
A) They use a hierarchical structure to make decisions based on input features.
B) They are inspired by the functioning of the human brain.
C) They make predictions based on similarities between new instances and previously seen examples.
D) They combine prior knowledge and observed data using Bayesian probability theory.
Which machine learning method is inspired by the structure and functioning of the human brain?
A) Decision Trees
B) Artificial Neural Networks (ANN)
C) Instance-Based Learning
D) Reinforcement Learning
Instance-Based Learning is also known as:
A) Decision Trees
B) Artificial Neural Networks (ANN)
C) Inductive Logic Programming
D) Lazy Learning
Bayesian methods in machine learning involve:
A) Using a hierarchical structure to make decisions based on input features.
B) Combining prior knowledge and observed data using Bayesian probability theory.
C) Making predictions based on similarities between new instances and previously seen examples.
D) Inducing general rules or hypotheses from observed examples.
Which machine learning method involves an agent learning to take actions in an environment to maximize cumulative rewards or minimize penalties?
A) Decision Trees
B) Instance-Based Learning
C) Reinforcement Learning
D) Genetic Algorithms
What does Inductive Logic Programming combine to induce general rules or hypotheses?
A) Logic programming and machine learning
B) Bayesian probability theory and observed data
C) Evolution and selection operations
D) Prior knowledge and observed data
Genetic Algorithms are inspired by:
A) The structure and functioning of the human brain
B) The process of natural selection and genetic evolution
C) Inductive logic programming
D) Maximizing cumulative rewards or minimizing penalties
Which machine learning method finds an optimal hyperplane to separate different classes?
A) Decision Trees
B) Support Vector Machines
C) Reinforcement Learning
D) Bayesian methods
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