How does Random Forest handle missing values in the dataset?
Practice Questions
Q1
How does Random Forest handle missing values in the dataset?
It ignores missing values completely
It uses mean imputation for missing values
It can use surrogate splits to handle missing values
It requires complete data without any missing values
Questions & Step-by-Step Solutions
How does Random Forest handle missing values in the dataset?
Step 1: Understand that Random Forest is a machine learning method that uses many decision trees to make predictions.
Step 2: Recognize that sometimes, the dataset may have missing values, meaning some information is not available.
Step 3: Learn that Random Forest can still work with these missing values by using something called 'surrogate splits'.
Step 4: Know that a surrogate split is an alternative way to split the data when the main feature is missing.
Step 5: When a decision tree encounters a missing value, it looks for the next best feature (the surrogate) to make a decision.
Step 6: This allows the Random Forest to continue making predictions even when some data is incomplete.
Random Forest – An ensemble learning method that constructs multiple decision trees and merges them to improve accuracy and control overfitting.
Missing Values – Data points that are not recorded or are absent in the dataset, which can affect the performance of machine learning models.
Surrogate Splits – Alternative splits used in decision trees to handle missing values by finding the best alternative feature to split on when the primary feature is missing.
