ISYE 6501, MIDTERM 1 LATEST ACTUAL EXAM WITH
VERIFIED QUESTIONS & CORRECT ANSWERS
Question 1.
what are parametric methods?
ANSWER
the form of the predictor (linear regression)
Question 2.
In simple forecasting with basic exponential smoothing what is the value of F_{t+i}
ANSWER
S_t
Question 3.
what does a smaller AIC encourage
ANSWER
higher likelihood and less parameters
Question 4.
What is an outlier?
ANSWER
A data point that is very different from the rest
Question 5.
What do descriptive questions ask?
ANSWER
What happened? (e.g., which customers are most alike)
,Question 6.
if there is a strong relationship between a predictor and the response what will it's p-value be
ANSWER
very low.
Question 7.
Tree-based approaches can be used for other models besides regression.
ANSWER
True. For example, a classification tree might have a different SVM or KNN model at each leaf.
It might even use SVM at some leaves and KNN
at others (though that's probably rare).
Question 8.
What is Cumulative sum (CUSUM) used for
ANSWER
detect in crease, decrease or both
Question 9.
why is the first L value of C set to 1
ANSWER
that indicates so seasonality in multiplicative relationship
Question 10.
What can clustering be used for?
ANSWER
grouping data points (e.g., market segmentation) and discovering groups in data points (e.g.,
personalized medicine
Question 11.
How many data points do you need for ARIMA to work?
ANSWER
40
,Question 12.
What is a soft classifier and when is it used?
ANSWER
In some cases, there won't be a line that separates all of the labeled examples. So we use a
classifier that minimizes the number of mistakes.
Question 13.
Name two factor-based models?
ANSWER
SVM and regression
Question 14.
When comparing models, if we use the same data to pick the best model as we do to estimate how
good the best one is, what is likely to happen?
ANSWER
The model will appear to be better than it really is.
The model with the highest measured performance is likely to be both good and lucky in its fit
to random patterns.
Question 15.
What is L
ANSWER
length of cycle. When we're taking daily observation then L is 7
Question 16.
When clustering for prediction how do we choose the prediction?
ANSWER
When we see a new point, we just choose whichever cluster center is closest.
, Question 17.
what is ARIMA(p,d,q) (0,0,0)
ANSWER
white noise
Question 18.
To maximize the distance between the two lines what do we need to minimize?
ANSWER
Question 19.
when does exponential smoothing work well
ANSWER
when the data is stationary (i.e., mean, variance and other measures are all expected to be
constant over time)
Question 20.
What is bias?
ANSWER
Bias is an error from erroneous assumption in the learning algorithm High bias can cause an
algorithm to miss the relevant relations between features and target outputs (underfitting)
Question 21.
How does the exponential smoothing formula weight more recent observations more than older ones?
ANSWER
(1 - alpha) < 1
VERIFIED QUESTIONS & CORRECT ANSWERS
Question 1.
what are parametric methods?
ANSWER
the form of the predictor (linear regression)
Question 2.
In simple forecasting with basic exponential smoothing what is the value of F_{t+i}
ANSWER
S_t
Question 3.
what does a smaller AIC encourage
ANSWER
higher likelihood and less parameters
Question 4.
What is an outlier?
ANSWER
A data point that is very different from the rest
Question 5.
What do descriptive questions ask?
ANSWER
What happened? (e.g., which customers are most alike)
,Question 6.
if there is a strong relationship between a predictor and the response what will it's p-value be
ANSWER
very low.
Question 7.
Tree-based approaches can be used for other models besides regression.
ANSWER
True. For example, a classification tree might have a different SVM or KNN model at each leaf.
It might even use SVM at some leaves and KNN
at others (though that's probably rare).
Question 8.
What is Cumulative sum (CUSUM) used for
ANSWER
detect in crease, decrease or both
Question 9.
why is the first L value of C set to 1
ANSWER
that indicates so seasonality in multiplicative relationship
Question 10.
What can clustering be used for?
ANSWER
grouping data points (e.g., market segmentation) and discovering groups in data points (e.g.,
personalized medicine
Question 11.
How many data points do you need for ARIMA to work?
ANSWER
40
,Question 12.
What is a soft classifier and when is it used?
ANSWER
In some cases, there won't be a line that separates all of the labeled examples. So we use a
classifier that minimizes the number of mistakes.
Question 13.
Name two factor-based models?
ANSWER
SVM and regression
Question 14.
When comparing models, if we use the same data to pick the best model as we do to estimate how
good the best one is, what is likely to happen?
ANSWER
The model will appear to be better than it really is.
The model with the highest measured performance is likely to be both good and lucky in its fit
to random patterns.
Question 15.
What is L
ANSWER
length of cycle. When we're taking daily observation then L is 7
Question 16.
When clustering for prediction how do we choose the prediction?
ANSWER
When we see a new point, we just choose whichever cluster center is closest.
, Question 17.
what is ARIMA(p,d,q) (0,0,0)
ANSWER
white noise
Question 18.
To maximize the distance between the two lines what do we need to minimize?
ANSWER
Question 19.
when does exponential smoothing work well
ANSWER
when the data is stationary (i.e., mean, variance and other measures are all expected to be
constant over time)
Question 20.
What is bias?
ANSWER
Bias is an error from erroneous assumption in the learning algorithm High bias can cause an
algorithm to miss the relevant relations between features and target outputs (underfitting)
Question 21.
How does the exponential smoothing formula weight more recent observations more than older ones?
ANSWER
(1 - alpha) < 1
