Now, here’s my question to you – do you know what these two processes represent? This is a classic example where collective decision making outperformed a single decision-making process. Then, the bank combines results from these multiple decision-making processes and decides to give the loan to the customer.Įven if this process took more time than the previous one, the bank profited using this method. Sometimes it checks for credit history first, and sometimes it checks for customer’s financial condition and loan amount first. Now, another loan application comes in a few days down the line but this time the bank comes up with a different strategy – multiple decision-making processes. Therefore, the bank lost the chance of making some money. Hence, the bank rejects the application.īut here’s the catch – the loan amount was very small for the bank’s immense coffers and they could have easily approved it in a very low-risk move. The bank checks the person’s credit history and their financial condition and finds that they haven’t re-paid the older loan yet. Suppose a bank has to approve a small loan amount for a customer and the bank needs to make a decision quickly. Let’s start with a thought experiment that will illustrate the difference between a decision tree and a random forest model. A Simple Analogy to Explain Decision Tree vs.
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