Kevin Brown is the management accountant at Boehm and Sons Bank. Some of his colleagues attended a conference recently where they learned that machine learning can be used to accurately predict loan performance. Excited by the possibilities, they asked Kevin to work with the data science team to develop a machine learning model that predicts whether a loan will repay or default. Kevin knows that defaulting loans have significantly hurt Boehm and Sons' profitability. Kevin tells the data science team that his colleagues will appreciate a model that is interpretable and easy to understand, so they decide to use a decision tree. The team selects a random sample of eight loans (three loans that defaulted and five loans that repaid) from Boehm and Sons' internal loan database and begins the analysis (Click the icon to view the plot of the data.) Read the requirements. Requirement 1. The figure provided identifies two possible places to make the first cut. Visually, is one of these cuts better than the other at separating the data? (Click the icon to view the figure.) For this requirement, simply make a mental note of (or jot down) which cut appears visually better than the other at separating the data