Description

Note: In case a problem requires programming, it should be programmed in Python. In Programming, you should use plain Python language, unless otherwise stated. For example, if the intention of a Problem is familiarity with numpy library, it will be clearly noted in that problem to use numpy. Please submit your homework through Gradescope.
Submissions: There are two steps to submitting your assignment on Gradescope:
1. HW05 Writeup: Submit a combined pdf file containing the answers to theoretical questions as well as the pdf form of the FILE.ipynb notebooks.
• To produce a pdf of your notebooks, you can first convert each of the .ipynb files to HTML.
• To do this, simply run: ipython nbconvert -to html FILE.ipynb for each of the notebooks, where FILE.ipynb is the notebook you want to convert. Then you can convert the HTML files to PDFs with your favorite web browser.
• If an assignment has theoretical and mathematical derivation, scan your handwritten solution and make a PDF file.
• Then concatenate them all together in your favorite PDF viewer/editor. The file name (FILE) for naming should be saved as HW-assignmentnumber-andrew-ID.pdf. For example for assignment 1, my FILE = HW-1-lkara.pdf
• Submit this final PDF on Gradescope, and make sure to tag the questions correctly!
2. HW05 Code: Submit a ZIP folder containing the FILE.ipynb notebooks for each of the programming questions. The ZIP folder containing your iPython notebook solutions should be named as HW-assignmentnumber-andrew-ID.zip
Q1: Decision Trees (30 pts)

(a) (10 pts) Consider two binary variables x and y having the joint distribution given in the table below.
x ↓,y → 0 1
0 1/3 1/3
1 0 1/3
Evaluate the following distributions and/or quantities
P(x), P(y), P(x|y), P(y|x), P(x,y),
H(x), H(y), H(x|y), H(y|x), IG(x|y).
(b) (20 pts) Consider the following decision tree. For the arrows: 1 = True, 0 = False.

(i) On a 2D plot whose axes are X1 and X2, draw the decision boundaries defined by this tree. Each leaf is labeled with a letter. Write this letter in the corresponding region of the instance space. Your plot should produce rectangular blocks.
(ii) Draw another decision tree that is syntactically different than the one shown above but definesthe same decision boundaries.
Q2: Decision Trees (35 pts)

Suppose you are given a simple dataset listed in the table below and need to learn a decision tree from the data. These are 8 instances, each with 3 binary attributes (X1,X2,X3) and a binary class label (Y ). You can solve this problem by hand. Use the data to answer the following questions.
Instance X1 X2 X3 Y
1 0 0 0 0
2 0 0 1 0
3 0 1 0 1
4 0 1 1 1
5 1 0 1 1
6 1 0 1 1
7 1 1 0 0
8 1 1 0 0
(a) (3 pts) Compute the entropy of the class label.
(b) (6 pts) Calculate the information gains of Y with respect to each of the three attributes. Which attribute should be selected for the root of the decision tree? Why?
(d) (8 pts) Run the algorithm by hand, compute your decisions tree. Draw your tree. Consult the lecture slides if you are unsure about drawing conventions. Considering all 8 instances to be training samples, compute the training error on the training dataset.
(e) (3 pts) Now suppose you are presented with new instances for which the class Y is unknown. Use your decision tree to predict the label of each instance listed below:
Instance X1 X2 X3 Y
9 1 1 1 ?
10 1 0 0 ?
11 0 1 1 ?
(f) (5 pts) For the decision tree you have learned so far, do you have any basis on which to evaluate if the tree is overfitting? Why or why not? How might you combat overfitting in a decision tree? (Answers will not be unique. Describe your favorite one in one or two sentences.)
(g) (5 pts) If the labels on Instances 6 and 7 were changed to 0 and 1, respectively, would the structure of the learned decision tree change? Why or why not? Also, would any of the leaf nodes change? Explain your reasoning.
(g) (5 pts) Now using Python, solve the original problem and show the constructed tree.
From the sklearn.tree library, you can use DecisionTreeClassifier and plot tree functions. Output the decisions of this tree for the data shown in part (e).
Q3: Classification and Regression Tree (CART) Implementation (35 pts)

This question provides a case where you can complete a coded CART model (a very common category of decision tree) to some real world data sets. The Python code uses recursion to implement the CART. You will not be asked to code the model from scratch, we will provide you with the base code and you just need to fill a small proportion of the code after you read and understand how the code works. After that, you will experiment your completed model as well as the sklearn library of CART on some datasets.
(a) (5 pts) Gini impurity: as has been covered in the class, Gini impurity describes how homogeneous a set of data is. A set of data is pure if all its members belong to the same class, while a set with many samples from many different classes will have a Gini close to 1. More formally the Gini impurity of n training samples split across k classes is defined as:
(1)
{snow: 6 days, rain: 10 days, cloudy: 10 days, sunny: 2 days, shower: 3 days}.
(b) (20 pts) Code completion: In the cart.py for Q3. You will find an almost completed implementation of CART from scratch. You have to put tree.py and wifi localization.txt under the same folder to run the code normally. In order to complete the code please do the following:
• Read the code: Read through the code forming the class DecisionTreeClassifier. You might want to pay attention to member functions:
def gini(self,y) and def grow tree(self,X,y,depth=0)
where you will be asked to complete a few lines of code to make the CART working. You might look at the brief introduction below about Python class construction if you are not familiar with it. If you skip this step, you are very likely to complete your code incorrectly.
• Complete the Gini impurity calculation: In the place annotated with #1.Your code goes here, fill in small piece of code to calculate the Gini impurity. You will need the variable self.n classes which is the number of classes you need to calculate the gini impurity. The code could be done within 3 lines.
• Complete the variables in the recursion function: the way you actually construct the CART is by using the member function def grow tree(self,X,y,depth=0) recursively. This means that you will call this function inside itself. This might sounds tricky if you have not implemented this before, but it is just an efficient way to traverse a tree structure: when you are growing the tree from a certain node of the CART by calling def grow tree(self,X,y,depth=0), you construct the branch of this node by calling this same function again inside itself for its children (two children for the decision tree case). But when you call this function inside itself, the variables {x,y,depth} have to be changed accordingly

Figure 1: CART Visualized Trees Sample.
to values of the children. Once you complete this part correctly, the CART code is good to go! Based on this, fill in the variables for the function in the place annotated with #2.Your code goes here. When submitting homework, please include a screenshot of your added code.
Hint: a brief introduction on how a Python class is constructed. Understanding this might help you complete the code easier. (https://www.w3schools.com/python/python_classes.asp)
(c) (10 pts) Experiment with the completed model: Now that you have a completed CART implementation. Please experiment it and compare against the sklearn results using two datasets. The corresponding scripts are on the Jupyter Notebook. Again, you have to put tree.py, cart.py and wifi localization.txt under the same folder to run the code normally. You need to do the following four tests:
• python3 cart.py –dataset=”iris” –max depth=2 –hide details
• python3 cart.py –dataset=”iris” –max depth=2 –hide details –use sklearn
• python3 cart.py –dataset=”wifi” –max depth=2 –hide details
• python3 cart.py –dataset=”wifi” –max depth=3 –use sklearn
You could run python instead of python3 if your default Python version is 3.x.x. Ideally, for the Python implementation, you should get visualized results like Fig. 1. Display the generated results based on these experiments on the Jupyter Notebook for grading.