Question: How do Decision Trees handle missing values?
Options:
They cannot handle missing values
By ignoring them completely
By using surrogate splits
By imputing values with the mean
Correct Answer: By using surrogate splits
Solution:
Decision Trees can use surrogate splits to handle missing values effectively.
How do Decision Trees handle missing values?
Practice Questions
Q1
How do Decision Trees handle missing values?
They cannot handle missing values
By ignoring them completely
By using surrogate splits
By imputing values with the mean
Questions & Step-by-Step Solutions
How do Decision Trees handle missing values?
Step 1: Understand that Decision Trees are a type of model used for making decisions based on data.
Step 2: Recognize that sometimes data can have missing values, which means some information is not available.
Step 3: Learn that Decision Trees can still make decisions even when there are missing values.
Step 4: Know that one way Decision Trees handle missing values is by using something called 'surrogate splits.'
Step 5: Surrogate splits are alternative ways to split the data when the main value is missing.
Step 6: The Decision Tree looks for other features (or columns) in the data that can help make a decision instead.
Step 7: This allows the Decision Tree to continue working and making predictions even with missing data.
Decision Trees and Missing Values – Decision Trees can manage missing values by using surrogate splits, which allow the model to make decisions based on alternative features when the primary feature is missing.
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