Week 5: CART Homework Assignment

Preliminary Information

Classification and Regression Trees (CART) are a machine learning algorithm that builds decision trees for classification and regression by splitting data based on feature values. A classification tree sorts data into categories. A regression tree predicts continuous values by splitting data based on feature values. A classification tree is a structural mapping of binary decisions that lead to a decision about the class (interpretation) of an object. Although many data analysts will simply call it a decision tree, it is more appropriately a type of decision tree that will lead the analyst to categorical decisions. A regression tree is a predictive model that utilizes one or more input variables and a single output variable that leads an analyst to make predictions. The output variable is numerical, whereas the input variables can be a mix of categorical and continuous variables. Regression trees are a type of decision tree that generally predicts numerical outcomes instead of classifications. Please complete this assignment by addressing the following questions.

Instructions

In a MS Word document, address each of the following:

1. Discuss the utilization of Python from the lab assignment and what you learned from the experience. (Minimum of 50 words, worth 10 points)
2. Interpret information resulting from CART analysis you completed in your two lab assignments. (Minimum of 50 words, worth 10 points).
3. Research on the internet and find one decision tree. Paste the tree result on your Word document with a reference as to where you found the tree. Then, interpret the information based on your analysis of the tree you located. (Minimum of 75 words, worth 15 points)
4. Finally, explain the limitations of CART analysis. Discuss how you can adjust for such limitations. (Minimum 75 words, worth 15 points)
5. Submit your assignment in a MS Word document.