In which scenario would Random Forests be preferred over a single Decision Tree?
Practice Questions
Q1
In which scenario would Random Forests be preferred over a single Decision Tree?
When interpretability is the main goal
When the dataset is small
When overfitting is a concern
When the model needs to run in real-time
Questions & Step-by-Step Solutions
In which scenario would Random Forests be preferred over a single Decision Tree?
Step 1: Understand what a Decision Tree is. It is a model that makes decisions based on asking a series of questions.
Step 2: Recognize that a single Decision Tree can easily fit the training data too closely, which is called overfitting. This means it may not perform well on new, unseen data.
Step 3: Learn that Random Forests use many Decision Trees instead of just one. They create a 'forest' of trees.
Step 4: Realize that by averaging the results of many trees, Random Forests can reduce the risk of overfitting.
Step 5: Conclude that Random Forests are preferred when you want a more reliable and robust model that performs better on new data.
