Description
Problem 1: Decision Trees for Spam Classification (25 points)
We’ll use the same data as in our earlier homework: In order to reduce my email load, I decide to implement a
machine learning algorithm to decide whether or not I should read an email, or simply file it away instead. To
train my model, I obtain the following data set of binary-valued features about each email, including whether I
know the author or not, whether the email is long or short, and whether it has any of several key words, along
with my final decision about whether to read it ( y = +1 for “read”, y = −1 for “discard”).
x1 x2 x3 x4 x5 y
know author? is long? has ‘research’ has ‘grade’ has ‘lottery’ ⇒ read?
0 0 1 1 0 -1
1 1 0 1 0 -1
0 1 1 1 1 -1
1 1 1 1 0 -1
0 1 0 0 0 -1
1 0 1 1 1 1
0 0 1 0 0 1
1 0 0 0 0 1
1 0 1 1 0 1
1 1 1 1 1 -1
In the case of any ties where both classes have equal probability, we will prefer to predict class +1.
1. Calculate the entropy H( y), in bits, of the binary class variable y. Hint: Your answer should be a number
between 0 and 1. (5 points)
2. Calculate the information gain for each feature xi
. Which feature should I split on for the root node of the
decision tree? (10 points)
3. Determine the complete decision tree that will be learned from these data. (The tree should perfectly classify
all training data.) Specify the tree by drawing it, or with a set of nested if-then-else statements. (10 points)
Problem 2: Decision Trees in Python (50 points)
In this problem, we will use our Kaggle in-class competition data to test decision trees on real data. Kaggle
is a website designed to host data prediction competitions; we will use it to gain some experience with more
realistic machine learning problems, and have an opportunity to compare methods and ideas amongst ourselves.
Our in-class Kaggle page is https://www.kaggle.com/c/uci-cs178-f20; you can join using the participation
URL: https://www.kaggle.com/t/bd21e4e5d61540888ed61f438bac55b8 Follow the instructions on the CS178
Canvas page to create an appropriate Kaggle account (if necessary), join our in-class competition, and download
the competition data. Note: although you will eventually form teams for the project, please do not merge yet, as
you will want to be able to submit individually for this homework. For convenience, the data are also included in
the HW4 code zip, in the data subdirectory.
1. The following code may be used to load the training features X and class labels Y :
Homework 4 UC Irvine 1/ 4
CS 178: Machine Learning & Data Mining Fall 2020
1 X = np.genfromtxt(‘data/X_train.txt’, delimiter=’,’)
2 Y = np.genfromtxt(‘data/Y_train.txt’, delimiter=’,’)
3 X,Y = ml.shuffleData(X,Y)
4 # and simlarly for test data features. Test target values are withheld for the
,→ competition.
The first 41 features are numeric (real-valued features); we will restrict our attention to these:
1 X = X[:,:41] # keep only the numeric features for now
Print the minimum, maximum, mean, and variance of each of the first 5 features. (5 points)
2. To enable us to do model selection, partition your training data X into training data Xtr,Ytr and validation
sets Xva,Yva of approximately equal size. Learn a decision tree classifier from the training data using the
method implemented in the mltools package (this may take a minute):
1 learner = ml.dtree.treeClassify(Xtr, Ytr, maxDepth=50)
Here, we set the maximum tree depth to 50 to avoid potential recursion limits or memory issues. Compute
and report your decision tree’s training and validation error rates. (5 points)
3. Now try varying the maxDepth parameter, which forces the tree learning algorithm to stop after at most that
many levels. Test maxDepth values in the range 0, 1, 2, …, 15 , and plot the training and validation
error rates versus maxDepth . Do models with higher maxDepth have higher or lower complexity? What
choice of maxDepth provides the best decision tree model? (10 points)
4. The minParent parameter controls the complexity of decision trees by lower bounding the amount of data
required to split nodes when learning. Fixing maxDepth=50 , compute and plot the training and validation
error rates for minParent values in the range 2.∧[0:13]=[1,2,4,8,…,8192] . Do models with higher
minParent have higher or lower complexity? What choice of minParent provides the best decision tree
model? (10 points)
5. (Not graded) A related control is minLeaf ; how does complexity control with minParent compare to
minLeaf ?
6. We discussed in class that we could understand our model’s performance as we vary our preference for false
positives compared to false negatives using the ROC curve, or summarize this curve using a scalar area under
curve (AUC) score. For the best decision tree model trained in the previous parts, use the roc function to
plot an ROC curve summarizing your classifier performance on the training points, and another ROC curve
summarizing your performance on the validation points. Then using the auc function, compute and report
the AUC scores for the training and validation data. (10 points)
7. Based on your results in the previous parts, pick maxDepth and minParent values that you think will
perform well. Retrain your decision tree model using all the data in X_train.txt . Score your performance
on the same data (accuracy rate and AUC).
Then, using code like the following, make predictions on the test points (feature vectors found in X_test.txt ),
and export your predictions in the format required by Kaggle:
1 learner = … # train a model using training data X,Y
2 Xte = np.genfromtxt(‘data/X_test.txt’, delimiter=’,’)
3 Yte = np.vstack((np.arange(Xte.shape[0]), learner.predictSoft(Xte)[:,1])).T
4 # Output a file with two columns, a row ID and a confidence in class 1:
5 np.savetxt(‘Y_submit.txt’,Yte,’%d, %.2f’,header=’Id,Predicted’,comment=”,delimiter=’,’
,→ )
Submit your predictions on all of the test data to Kaggle, and include your Kaggle username and leaderboard
AUC in your homework solutions. (10 points)
Note that we use predictSoft to output probabilistic predictions (that test examples are members of class 1) for
upload to Kaggle. While you may also use “hard” predictions (class values), accounting for the learned model’s
confidence in each prediction will produce a smoother ROC curve and (usually) a better AUC score.
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CS 178: Machine Learning & Data Mining Fall 2020
Problem 3: Ensemble Methods (20 points)
Choose either part of this question to answer (your choice): a random forest classifier, which is a bagged ensemble
of decision trees; or an boosted ensemble of regression trees learned with gradient boosting.
In Python, it is easy to keep a list of different learners, even of different types, for use in an ensemble predictor:
1 ensemble[i] = ml.treeClassify(Xb,Yb,…) # save ensemble member “i” in a cell array
2 # …
3 ensemble[i].predict(Xv,Yv); # find the predictions for ensemble member “i”
Option 1: Random forests:
Random Forests are bagged collections of decision trees, which select their decision nodes from randomly chosen
subsets of the possible features (rather than all features). You can implement this easily in treeClassify using
option ’nFeatures’ =n, where n is the number of features to select from (e.g., n = 50 or n = 60 if there are
90-some features); you’ll write a for-loop to build the ensemble members, and another to compute the prediction
of the ensemble.
1. Using your validation split, learn a bagged ensemble of decision trees on the training data and evaluate
validation performance. (See the pseudocode from lecture slides.) For your individual learners, use little
complexity control (depth cutoff 15+, minLeaf 4, etc.), since the bagging will be used to control overfitting
instead. For the bootstrap process, draw the same number of data as in your training set after the validation
split (M0 = M in the pseudocode). You may find ml.bootstrapData() helpful, although it is very easy to
do yourself. Plot the training and validation error as a function of the number of learners you include in the
ensemble, for (at least) 1, 5, 10, 25 learners. (You may find it more computationally efficient to simply learn
25 ensemble members first, and then evaluate the results using only a few of them; this will give the same
results as only learning the few that you need.)
2. Now choose an ensemble size and build an ensemble using the full training set, make predictions on the test
data, and evaluate (via Kaggle’s leaderboard) and report your performance.
Option 2: Gradient boosting:
Gradient boosted trees are boosted collections of decision trees, which are build sequentially to predict the residual
error in the current ensemble. You’ll write a for-loop to build the ensemble members, and another to compute the
prediction of the ensemble.
Since this is a classification problem, we will do gradient boosting on a logistic negative log likelihood loss,
i.e., we will regress the log-odds ratio in a manner similar to logistic regression (except that, instead of a linear
regression on the log-odds, we will use a collection of decision trees). Use treeRegress to fit each learner to the
(real-valued) log-odds update; treeRegress works analagously to treeClassify .
In practice, start out with a baseline predictor f (x) = 0, and compute probabilies p( y = 1) = σ(f (x)) where
σ is the usual logistic function. Then, update f (x) by regressing the derivative of J:
dJ(i)
d f (i)
=
¨
1 − σ(f (x
(i)
)) y
(i) = 1
−σ(f (x
(i)
)) y
(i) = 0
Fit dJ using a regression tree t(x), and update f (x) = f (x) + αt(x) for some step size α.
1. Using your validation split, learn a gradient boosted ensemble of decision trees on the training data and
evaluate validation performance. (See the pseudocode from lecture slides.) For your individual learners,
use very strong complexity control (depth cutoff 2–3, or large minParent, etc.), since the boosting process
will be adding complexity to the overall learner. Plot the training and validation error as a function of the
number of learners you include in the ensemble, for (at least) 1,5,10,25 learners. (You may find it more
computationally efficient to simply learn 25 ensemble members, and then evaluate the results using fewer of
them.)
2. Now choose an ensemble size and repeat on the full training data, make predictions on the test data, and
evaluate your accuracy and AUC scores. Report your performance.
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CS 178: Machine Learning & Data Mining Fall 2020
Problem 4: Statement of Collaboration (5 points)
It is mandatory to include a Statement of Collaboration in each submission, that follows the guidelines below.
Include the names of everyone involved in the discussions (especially in-person ones), and what was discussed.
All students are required to follow the academic honesty guidelines posted on the course website. For
programming assignments in particular, I encourage students to organize (perhaps using Piazza) to discuss the
task descriptions, requirements, possible bugs in the support code, and the relevant technical content before they
start working on it. However, you should not discuss the specific solutions, and as a guiding principle, you are
not allowed to take anything written or drawn away from these discussions (no photographs of the blackboard,
written notes, referring to Piazza, etc.). Especially after you have started working on the assignment, try to restrict
the discussion to Piazza as much as possible, so that there is no doubt as to the extent of your collaboration.
Homework 4 UC Irvine 4/ 4
