Homework #2 Decision Tree

UCLA CS 145 Homework #2 
Note: ● You are expected to submit both a report and code. 
1. Decision Tree
1.1. Construct a decision tree for samples from the congressional voting records dataset with the first three attributes in the UCI machine learning repository. Information gain is used to select the attributes. Please write down the major steps in the construction process, i.e., you need to show the information gain for each candidate attribute when a new node is created in the tree.
Class Vote for handicappedinfants?
Vote for water-projectcost-sharing
Vote for budget-resolutionadoption Democrat Y N Y Republican N Y N Democrat Y Y Y Republican N Y N Democrat N Y N Democrat N Y Y Democrat Y N Y Democrat Y Y Y Republican N Y Y Republican Y Y N Democrat N N Y Republican N Y N Republican N N N Democrat N N Y
Republican N N N Republican N Y N Democrat Y N Y Democrat Y N Y Republican N N N Republican Y N Y
1.2 In DecisionTree\DecisionTree.py, fill in the missing lines for building a decision tree model, using (a) information gain and (b) gain ratio. Output the accuracy on the test data and compare the two versions of decision tree. Which attribute selection measure do you want to choose for the dataset and why.
2. Support Vector Machine 2.1 The table shown below contains 20 data points and their class labels.
Point # x1 x2 Class (y)
1 0.52 -1 1
2 0.91 0.32 1
3 -1.48 1.23 1
4 0.01 1.44 1
5 -0.46 -0.37 1
6 0.41 2.04 1
7 0.53 0.77 1
8 -1.21 -1.1 1
9 -0.39 0.96 1
10 -0.96 0.08 1
11 2.46 2.59 -1
12 3.05 2.87 -1
13 2.2 3.04 -1
14 1.89 2.64 -1
15 4.51 -0.52 -1
16 3.06 1.3 -1
17 3.16 -0.56 -1
18 2.05 1.54 -1
19 2.34 0.72 -1
20 2.94 0.13 -1 Suppose by solving the dual form of the quadratic programming of svm, we can derive the 𝛼𝛼𝑖𝑖’s for each data point as follows: 𝛼𝛼2= 0.5084 𝛼𝛼6= 0.4625 𝛼𝛼18 = 0.9709 Others = 0 (a) Please point out the support vectors in the training points. (b) Calculate the normal vector of the hyperplane: 𝑤𝑤 (c) Calculate the bias b, according to 𝑏𝑏 =𝛴𝛴𝑘𝑘:𝛼𝛼𝑘𝑘≠0 (𝑦𝑦𝑘𝑘− 𝑤𝑤𝑇𝑇𝑥𝑥𝑘𝑘)/𝑁𝑁𝑘𝑘, where 𝑥𝑥𝑘𝑘=( 𝑥𝑥𝑘𝑘1,𝑥𝑥𝑘𝑘2)𝑇𝑇 indicates the support vectors and 𝑁𝑁𝑘𝑘 is the total number of support vectors. (d) Write down the learned decision boundary function 𝑓𝑓(𝑥𝑥) =𝑤𝑤𝑇𝑇𝑥𝑥+𝑏𝑏 (the hyperplane)
by substituting 𝑤𝑤 and 𝑏𝑏 with learned values in the formula. (e) Suppose there is a new data point 𝑥𝑥=(−1,2), please use the decision boundary to predict its class label. (f) Show a plot of the data points and your decision boundary line (x1 feature on x-axis, x2 feature on y-axis) in your report. Plot both data points and decision boundary in the same graph, and use different colors to represent points in different classes (y).

2.2 In SVM\svm.py, fill in the missing lines for support vector machines. Output the accuracy on the test data and compare (a) hard margin and soft margin SVM for linear classifier; and (b) different kernels for soft margin SVM. Which model do you want to choose?