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What is the role of feature importance in Random Forest?
Practice Questions
Q1
What is the role of feature importance in Random Forest?
To determine the number of trees to use.
To identify which features contribute most to the model's predictions.
To select the best hyperparameters.
To visualize the decision boundaries.
Questions & Step-by-Step Solutions
What is the role of feature importance in Random Forest?
Steps
Concepts
Step 1: Understand that a Random Forest is a type of machine learning model that uses many decision trees to make predictions.
Step 2: Realize that each decision tree in the Random Forest looks at different features (or variables) to make decisions.
Step 3: Feature importance measures how much each feature contributes to the accuracy of the model's predictions.
Step 4: Higher feature importance means that the feature is more useful for making predictions.
Step 5: By knowing which features are important, we can focus on the most relevant data and ignore less important ones.
Step 6: This helps in simplifying the model, improving performance, and making it easier to understand.
No concepts available.
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