[SOLVED] Cs-349 homework 1: decision trees
5.0
1 customer review
Digital download
Digital download
$25.00
- Availability
- In stock
- Checkout
- One item
Need a hand?
Message us on WhatsApp for payment or download support.
discuss the homework with other groups, but do not take any written record from the discussions.
Also, do not copy any source code from the Web or use code generated by AI.The algorithm you should implement is the same as that given in the decision tree lecture slides
(slide 4-5, the “ID3” algorithm). We have written code to read in the data for you (parse.py). It
represents each example as a dictionary, with attributes stored as key:value pairs. The target output
is stored as an attribute with the key “Class”.Guidelines
You should use Information Gain for choosing which attribute to split on.
You must handle missing attributes, but exactly how is up to you.
You must implement some kind of pruning, but exactly how is up to you.
You must adhere to the signature in the ID3.py file. In particular, you must implement the
four given methods as described in that file. You can and should define additional methods
in ID3.py, and additional fields in the Node class. You do not need to handle numeric attributes, but your code should work for categorical
attributes with an arbitrary number of attribute values.
Do not import any modules from outside the Python standard library. If you need other
modules to produce e.g. graphs, write that code in a separate source file. (4.0 points) Complete the decision tree code. You should use Python 3.6 or later. Implement
the four methods in ID3.py, adding new methods as necessary. You will also need to
change node.py. We have included a few tests in unit_tests.py that you can run individually,
to check your methods. Further, we have also included a file mini_auto_grader.py that gives
you an idea of the kinds of tests we will run to grade your methods.NOTE: that depending
on how you implement pruning, it’s possible (albeit unlikely) that the pruning test will not
pass even if your implementation is acceptable. We will test you code on an unseen dataset
with random training, validation and test sets. The tennis.data dataset is also provided to
help you debug your code on a much smaller and simpler dataset. Create a PDF document with the answers to the following questions (try to keep you
responses to a few sentences or a single paragraph for each question):
1. (0.25 points) Did you alter the Node data structure? If so, how and why?
2. (0.25 points) How did you handle missing attributes, and why did you choose this
strategy?3. (0.5 points) How did you perform pruning, and why did you choose this strategy?
4. (2.0 points) Now you will try your learner on the house_votes_84.data, and plot
learning curves. Specifically, you should experiment under two settings: with pruning,
and without pruning. Use training set sizes ranging between 10 and 300 examples. For
each training size you choose, perform 100 random runs, for each run testing on all
examples not used for training (see testPruningOnHouseData from unit_tests.py for
one example of this). Plot the average accuracy of the 100 runs as one point on a
learning curve (x-axis = number of training examples, y-axis = accuracy on test data).Connect the points to show one line representing accuracy with pruning, the
other without. Include your plot in your pdf, and answer two questions:
a. What is the general trend of both lines as training set size increases, and why
does this make sense?b. How does the advantage of pruning change as the data set size increases? Does
this make sense, and why or why not?Note: depending on your particular approach, pruning may not improve accuracy
consistently or may decrease it (especially for small data set sizes). You can still
receive full credit for this as long as your approach is reasonable and correctly
implemented.5. (1.0 points) Use your ID3 code to learn a decision tree on cars_train.data. Report
accuracy on the cars_train.data, cars_valid.data and cars_test.data datasets. If
you accuracy on cars_train.data is less than 100%, explain how this can happen.
Prune the decision tree learned on cars_train.data using cars_valid.data. Run your
pruned decision tree on cars_test.data, and explain the resulting accuracy on all
three datasets.6. (2.0 points) Use your ID3 code to construct a Random Forest classifier using
the candy.data dataset. You can construct any number of random trees using methods
of your choosing. Justify your design choices and compare results to a single decision
tree constructed using the ID3 algorithm.Suggestion: You may find it helpful work with Weka (https://www.cs.waikato.ac.nz/ml/weka). To
work with ID3 in Weka, you may need to install the simpleEducationalLearningSchemes. A large
number of Weka tutorials exist on the Web.Submission Instructions
Turn in your homework as a single zip file, in Canvas. Specifically:
1. Create a single pdf file ps1.pdf with the answers to the questions above, and your graphs.
2. Create a single ZIP file containing:
o ps1.pdf
o All of your .py code files3. Turn the zip file in under Homework #1 in Canvas.
Note: You may also complete the assignment and include all responses in a single Jupyter
Notebook in a single ZIP file.
Good luck, and have fun!