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In which scenario would Random Forests be preferred over Decision Trees?
Practice Questions
Q1
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
Questions & Step-by-Step Solutions
In which scenario would Random Forests be preferred over Decision Trees?
Steps
Concepts
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.
No concepts available.
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