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In which scenario would you prefer using a Decision Tree over a Random Forest?
Practice Questions
Q1
In which scenario would you prefer using a Decision Tree over a Random Forest?
When interpretability is crucial.
When you have a very large dataset.
When you need high accuracy.
When computational resources are limited.
Questions & Step-by-Step Solutions
In which scenario would you prefer using a Decision Tree over a Random Forest?
Steps
Concepts
Step 1: Understand what a Decision Tree is. It is a simple model that makes decisions based on asking a series of questions.
Step 2: Know what a Random Forest is. It is a collection of many Decision Trees that work together to make a decision.
Step 3: Identify when you need to explain your model's decisions clearly. This is important in fields like healthcare or finance.
Step 4: Choose a Decision Tree if you need a model that is easy to understand and explain to others.
Step 5: Use a Random Forest if you want better accuracy and can sacrifice some interpretability.
No concepts available.
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