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.
