In which scenario would you prefer using a Random Forest over a single Decision Tree?

1 question

1 item

Q: In which scenario would you prefer using a Random Forest over a single Decision Tree?

Solution: Random Forests are preferred when higher accuracy and robustness are needed, especially in larger datasets.

Steps: 6

Step 1: Understand what a Decision Tree is. It is a simple model that makes decisions based on a series of questions.
Step 2: Recognize that a single Decision Tree can be prone to overfitting, meaning it might perform well on training data but 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 Forests reduce the risk of overfitting by averaging the results of multiple trees.
Step 5: Identify scenarios where you have a large dataset or complex data patterns, where a single Decision Tree might struggle.
Step 6: Conclude that in these scenarios, using a Random Forest will likely give you better accuracy and more reliable predictions.