Decision trees is an example for Supervised Machine Learning Unsupervised Machine Learning Semi-Supervised Machine Learning Reinforcement Learning
Question
Decision trees is an example for
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Reinforcement Learning
Solution
Understanding Decision Trees
Decision trees are a popular algorithm in the field of machine learning that help in making decisions based on certain criteria. They are particularly known for their intuitive graphical representation, resembling a tree structure where each node represents a decision based on an attribute.
Final Answer
Decision trees are an example of Supervised Machine Learning. This is because they use labeled data to learn the relationship between input features and the output label, allowing them to make predictions on unseen data. The training process involves splitting the data based on feature values to create branches and leaves that represent decisions and outcomes, respectively.
Similar Questions
Decision trees is an example for Supervised Machine Learning Unsupervised Machine Learning Semi-Supervised Machine Learning Reinforcement Learning
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Explain the concept of a decision tree and its applications. Provide an example to illustrate how decision trees are used in real-life scenarios.
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