In the context of Decision Trees, what does 'feature importance' refer to?
Practice Questions
1 question
Q1
In the context of Decision Trees, what does 'feature importance' refer to?
The number of times a feature is used in the tree.
The contribution of a feature to the model's predictions.
The correlation of a feature with the target variable.
The depth of a feature in the tree.
Feature importance refers to the contribution of a feature to the model's predictions, indicating how much it helps in making accurate decisions.
Questions & Step-by-step Solutions
1 item
Q
Q: In the context of Decision Trees, what does 'feature importance' refer to?
Solution: Feature importance refers to the contribution of a feature to the model's predictions, indicating how much it helps in making accurate decisions.
Steps: 4
Step 1: Understand that a feature is a piece of information used to make predictions in a decision tree.
Step 2: Realize that feature importance measures how much each feature helps the decision tree make accurate predictions.
Step 3: Know that a higher feature importance means that feature is more useful for making decisions.
Step 4: Remember that feature importance helps us understand which features are the most influential in the model.
