Step 1: Understand that Random Forest is a machine learning method that uses many decision trees to make predictions.
Step 2: Recognize that sometimes, in real-world data, some values can be missing.
Step 3: Learn that Random Forest can still make predictions even if some data is missing.
Step 4: Know that it does this 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: This means that Random Forest looks for other similar features in the data to help make a decision.
Step 7: As a result, Random Forest can still provide accurate predictions despite missing values.
Random Forest – An ensemble learning method that constructs multiple decision trees and merges them to improve accuracy and control overfitting.
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.
Handling Missing Values – The process of managing incomplete data in datasets, which is crucial for accurate predictions in machine learning models.
