Description
1 General Knowledge (15 pts)
1. ( 3 pts) Derive an expression for expectedLoss involving Bias, variance, and noise.
2. ( 3 pts) Explain how to use cross-validation to estimate each of the terms above.
3. (4 pts) Bias in a classifier means that the probability of classifying a new data point
drawn from the same distribution as the training data yields (xnew ∝ Dtrain) will be
labeled one category more than another.
(a) What aspects of the training data affect classfier bias?
(b) How does the hinge loss function in an SVM handle bias?
(c) Which parameters of an SVM affect bias on test data? How does increasing or
decreasing these parameters affect bias?
4. (5 pts) Consider a naive-Bayes generative model for the problem of classifying samples
{(x1, y1), …,(xn, yn)}, xi ∈ R
p and yi ∈ {1, . . . , K}, where the marginal distribution
of each feature is modeled as a univariate Gaussian, i.e., p(xij |yi = k) ∼ N (µjk, σ2
jk),
where k represents the class label. Assuming all parameters have been estimated,
clearly describe how such a naive-Bayes model will do classification on a test point
xtest.
2 Experiments (15 pts)
Imagine we are using 10-fold cross-validation to tune a parameter θ of a machine learning
algorithm using training set data for parameter estimation, and using the held-out fold to
evaluate test performance of different values of θ. This produces 10 models,{h1, …, h10};
each model hi has its own value θi for that parameter, and corresponding error ei
. Let
k = arg mini ei be the index of the model with the lowest error. What is the best procedure
for going from these 10 models individual to a single model that we can apply to the test
data?
1
a) Choose the model hk?
b) weight the predictions of each model by wi = exp(−ei)?
c) Set θ = θk, then update by training on the held-out data.
Clearly explain your choice and reasoning.
3 Kernels (15 pts)
Let {(x1, y1), …,(xn, yn)} be a dataset of n-samples for regression with xi ∈ R
p and yi ∈ R.
Consider a regularized regression approach to the problem:
w∗ = min
w∈Rp
1
2
X
i=1:n
(yi − wT xi)
2 + λwT w
This problem is known as ridge regression. Using a kernel trick we can write the solution
to this problem in a form where we can use weights on either the feature dimensions or
the data points. Rewriting the expression in terms of data points allows us to generalize
the regression solution to nonlinear forms. To better understand the problem, remember
that a data matrix X can be viewed in either row or column form, where rows are data
points and columns feature dimensions. A regression solution weighting rows is suitable
for a kernel form of a solution.
1. (5 pts) using notation y ∈ R
n
for the vector of responses generated by stacking labels
into a vector, and X ∈ R
nxp the matrix of features, rewrite the objective function
above in vector matrix form, and find the a closed form solution for w∗
. Is the
solution valid for n < p?
2. (5 pts) Show that the solution can be kernelized (i.e. that w∗ =
P
i=1:n αik(xi
, ·) ) for
some function k(x, ·) you need to derive. The trick is the derivation is a matrix inverse
identity: (A−1 + BT C
−1B)
−1BT C
−1 = ABT
(BABT + C)
−1
. In your application,
X = B, C = I and A = λI. The point of using the inverse is to convert your solution
from it’s standard form into one where you use XXT
, which creates an inner product
matrix of size data point by data point. By applying the resulting solution to a new
feature vector x, show that w
T x can be written in kernel form as above.
3. (5 pts) Use the kernelization result to derive ridge regression for fitting polynomials
to order m using a polynomial kernel function.
4 Gradient Descent (15 pts)
Consider the following regularized logistic regression problem:
w∗ = min
w∈Rp
L(w)
2
where
L(w) = 1
n
X
i=1:n
−yiwT xi + log
1 + exp
−wT xi
+ λG(w)
where G(w) is a (possibly non-smooth) regularization function.
1. (5 pts) Derive the subgradient for L(w) if G(w) = P
i=1:p
(a|wi
|
1
2 + b)
2
2. (5 pts) Derive a subgradient stochastic gradient descent algorithm for when
P
G(w) =
i=1:p
|wi
|
3. (5 pts) If G(w) = 1
2
P
i=1:p w
2
i
, what is the expected runtimes for Gradient descent
and Stochastic Gradient Descent in terms of (n, )
5 Boosting (15 pts)
The problem considers the adaboost algorithm.
1. (5 points) Describe the adaboost algorithm, using pseudocode. Clearly discuss the
variables and any assumptions on them.
2. (5 points) For adaboost, let αt denote the weight on the weak hypothesis Gt
in step
t. How is αt selected as the solution to an optimization problem? Clearly explain
your answer.
3. (5 points) Recall that adaboost can be viewed as minimizing a suitable upper bound
loss function on the “true” loss [y 6= h(x)], using the upper bound function C(yh(x) =
exp(−yh(x)). Can one choose C(·) to be the hinge loss?, i.e.,
C(yh(x)) = max(0, 1 − yh(x))
Explain your answer.
6 Adaboost (25 pts)
At Paul’s house, we need help with deciding whether a dog adoption candidate should be
brought home. Use following data set to to learn a committee machine via Adaboost to
predict whether the doggie are Adoptable (yes) or (no) based on their Price, Potty training
preparedness level (Normal or Low), Previous Incidents of Carpet Damage (2, 3, or 4), and
hair color (Brown/White or Yellow). Use a sequence of weak learners to predict the target
variable Adoptable. Each weak learner can only classify using one dimension. Use up to
ten learners, features can be used in any order and you choose how to assign features to
learners.
Item
Doggie Potty Training Prep (weeks) Price ($) Carpet Damage Color Adoptable
Tribi 3 13 0 Brown/White yes
Ross 1 0.01 1 Brown/White no
Rachael 1 92.50 2 Yellow no
Chandler 2 33.33 1 Brown/White no
Phoebe 3 8.99 3 Yellow no
Monica 2 8.99 0 Brown/White yes
Matt 3 13.65 0 Brown/White yes
David 1 0.01 2 Yellow no
Jenyfur 1 92.50 2 Brown/White no
Winona 2 33.33 2 Yellow no
Boo 3 8.99 1 Brown/White yes
Beau 2 12.49 2 Brown/White no
Table 1: Background data for the adaboost problem.
1. (5 points) Using the definition of weak learner, explain which features are eligible for
assignment to weak learners?
2. (15 points) Implement adaboost and run for no more than 10 rounds. Using your
adaboost committee machine trained on the data above, what should I do with Joey?
Joey has 3 weeks potty training, costs 0 dollars, has 0 instances of previous carpet
damage and is Brown/White. Adopt or no?
3. (2 points) Describe the first 3 stages of your algorithm as a decision tree.
4. (3 points) How much do you think you could benefit from additional learners? Answer
by explaining how you might determine how many learners are needed.
4
