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In which scenario would Random Forests be preferred over Decision Trees?
In which scenario would Random Forests be preferred over Decision Trees?
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Practice Questions
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In which scenario would Random Forests be preferred over Decision Trees?
When interpretability is crucial
When the dataset is small
When overfitting is a concern
When the model needs to be simple
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Random Forests reduce overfitting by averaging multiple Decision Trees, making them more robust.
Questions & Step-by-step Solutions
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Q
Q: In which scenario would Random Forests be preferred over Decision Trees?
Solution:
Random Forests reduce overfitting by averaging multiple Decision Trees, making them more robust.
Steps: 5
Show Steps
Step 1: Understand what Decision Trees are. They are simple models that make decisions based on questions about the data.
Step 2: Recognize that Decision Trees can easily overfit the data, meaning they can become too complex and perform poorly on new data.
Step 3: Learn that Random Forests are a collection of many Decision Trees working together.
Step 4: Realize that Random Forests reduce overfitting by averaging the results of multiple Decision Trees, which helps improve accuracy.
Step 5: Conclude that Random Forests are preferred when you want a more reliable model that performs better on unseen data.
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