What is Random Forest Classification?
The Random Forest Classification model constructs many decision trees wherein each tree votes and outputs the most popular class as the prediction result.
Random Forest Classification output helps identify important factors impacting the dependent variable and the nature of relationship between each of these factors and dependent variable. Random Forest Classification is limited to predicting categorical output so the dependent variable has to be categorical in nature. The minimum sample size is 20 cases per independent variable.
To further clarify the use of the Random Forest Classification model, let’s look at a sample customer churn analysis to predict the likelihood of customers to churn based upon important factors.
How Can Random Forest Classification Be Helpful for Business Analysis?
Explore the use cases below, to better understand the value of Random Forest Classification.
Business Use Case 1
Business Problem: Predict loan default.
Based on the historical data related to credit card payments, loan payments, existing loan status, job status we want to classify/divide the customers into defaulters and non defaulters.
- Default Status
- Home ownership status
- Existing loan status
- Account Balance
The predictive model will help us identify whether a customer fails to repay the loan depending on certain factors, which would lead to easier identification of risky customers and help the bank avert the risk delinquencies.
Business Use Case 2
Business Problem: Predict quality of Red Wine.
The data is a result of analysis to determine the quality of the red wine based upon chemicals it consists of.
- Quality category
- Citric Acid
- Residual Sugar
Using random forest classification, we can determine the quality of red wine (high, low) based upon its influential chemical attributes.
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