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What type of learning does a Decision Tree primarily use?
What type of learning does a Decision Tree primarily use?
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What type of learning does a Decision Tree primarily use?
Unsupervised Learning
Reinforcement Learning
Supervised Learning
Semi-supervised Learning
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Decision Trees primarily use Supervised Learning, where the model is trained on labeled data.
Questions & Step-by-step Solutions
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Q: What type of learning does a Decision Tree primarily use?
Solution:
Decision Trees primarily use Supervised Learning, where the model is trained on labeled data.
Steps: 5
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Step 1: Understand what a Decision Tree is. It is a model used to make decisions based on data.
Step 2: Learn about the types of learning in machine learning. There are mainly two types: Supervised Learning and Unsupervised Learning.
Step 3: Know that Supervised Learning means the model is trained on data that has labels (answers).
Step 4: Realize that Decision Trees are trained using labeled data, which helps them learn how to make decisions.
Step 5: Conclude that since Decision Trees use labeled data for training, they primarily use Supervised Learning.
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