What is a decision tree and what are its advantages?
An important advantage of the decision tree is that is a human-readable representation, and in case someone wants to know why they could not have a loan is reasonably easy to read and to interpreter just following the path from top to bottom.
For the multilayer perceptron the final hyperparameters are:
The correctly classified instance is…The result is…
A multilayer perceptron is called “black box” as it is not so easy to be interpreted by a human. Information about the function used by the model is not accessible. The number of layers and units per layer are known however it is far from being complete information.
Once you have a solution, show how you verified its robustness.
For the two different techniques report on their comparative ability to predict a defaulted loan, and also on how easy it would be for the insurance company to understand the model and the reasons behind each prediction it makes.
The test error is higher in predicting defaulted loan that is really paid rather than the opposite, and that is exactly what is better for the customer.
The ROC (Receiver Operating Characteristic) confront FP Rate and TP Rate to establish the ability of the model to split the information correctly. Closer to 1 better. It is usually ok if the value is more than 0.7. Although in this case, the model is slightly under, the complexity of the task can justify it.