In a Random Forest, what is the purpose of using multiple Decision Trees?
Practice Questions
Q1
In a Random Forest, what is the purpose of using multiple Decision Trees?
To increase the model's complexity
To reduce overfitting and improve accuracy
To simplify the model
To ensure all trees are identical
Questions & Step-by-Step Solutions
In a Random Forest, what is the purpose of using multiple Decision Trees?
Step 1: Understand what a Decision Tree is. A Decision Tree is a model that makes decisions based on asking a series of questions about the data.
Step 2: Recognize that a single Decision Tree can sometimes make mistakes, especially if it learns too much from the training data (this is called overfitting).
Step 3: Learn that a Random Forest is a collection of many Decision Trees working together.
Step 4: Realize that by using multiple Decision Trees, the Random Forest can average their predictions, which helps to correct individual mistakes.
Step 5: Understand that this averaging process makes the model more reliable and accurate, leading to better performance on new data.
