What is the primary advantage of using Random Forests over a single Decision Tree?
Practice Questions
1 question
Q1
What is the primary advantage of using Random Forests over a single Decision Tree?
Random Forests are easier to interpret.
Random Forests reduce overfitting by averaging multiple trees.
Random Forests require less computational power.
Random Forests can only handle categorical data.
Random Forests reduce overfitting by averaging the predictions of multiple decision trees, leading to better generalization on unseen data.
Questions & Step-by-step Solutions
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Q
Q: What is the primary advantage of using Random Forests over a single Decision Tree?
Solution: Random Forests reduce overfitting by averaging the predictions of multiple decision trees, leading to better generalization on unseen data.
Steps: 5
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 Random Forests use many Decision Trees instead of just one. This means they can make better predictions by combining the results of all the trees.
Step 4: Realize that by averaging the predictions from multiple trees, Random Forests can reduce the chances of making mistakes that a single tree might make.
Step 5: Conclude that the primary advantage of Random Forests is that they provide better accuracy and generalization on new, unseen data compared to a single Decision Tree.
