In which scenario would you prefer using a Random Forest over a Decision Tree?
Practice Questions
Q1
In which scenario would you prefer using a Random Forest over a Decision Tree?
When interpretability is the main concern.
When you have a small dataset.
When you need high accuracy and robustness.
When computational resources are limited.
Questions & Step-by-Step Solutions
In which scenario would you prefer using a Random Forest over a Decision Tree?
Step 1: Understand what a Decision Tree is. It is a simple model that makes decisions based on questions about the data.
Step 2: Recognize that Decision Trees can easily overfit, meaning they can become too complex and perform poorly on new data.
Step 3: Learn about Random Forest, which is a collection of many Decision Trees working together.
Step 4: Note that Random Forest reduces the risk of overfitting by averaging the results of multiple trees.
Step 5: Identify scenarios where you have a large dataset with many features, as Random Forest can handle this better than a single Decision Tree.
Step 6: Conclude that if you want higher accuracy and more reliable predictions, especially with complex data, you should choose Random Forest over a Decision Tree.
