Midterm Exam1: ISYE6501 / ISYE 6501 (Latest
Update ) Intro to Analytics
Modeling | Questions & Answers | Grade A |
100% Correct. Georgia Tech
1. What does SVM stand for?: Support Vector Machine
2. Is written text structured or unstructured?: Unstructured
3. When we increase the sum of the square of the coefficients we...:
Decrease the distance between the lines
4. In SVM soft classifier we tradeoff between maximizing ___ and
minimizing ___: margin and errors
5. If lambda gets small what gets emphasized, large margin or
minimizing training error?,: Minimizing errors.
6. What is a support vector?: A point that holds up a shape.
7. Does ...[(Ta-1)+1/3(a+1)] move an SVM classifier up or down?: Up
8. How do you make errors more costly in a soft SVM classifier?:
include a multiplier for the point-error term.
1
,9. If an SVM coefficient is very close to zero...: that term is not very
important to the classification.
10. What is the difference between standardization and scaling?: Scaling
is bounded in range. Standardization is scaling to a normal distribution.
Standardization is the (value - factor mean) / (factor standard deviation)
11. What is the 2-norm?: Euclidean distance
12. What is the 1-norm?: The rectilinear (Manhattan) distance
13. What is the infinity norm?: The value of the largest dimension
14. Measuring the quality of a model is called?: Validation
15. What does a confusion matrix show?: The performance of a
classification model.
16. A time series outlier that seems "off the curve" is called a...:
contextual outlier.
17. A data element that is different from all other data in a set is called
a...: point outlier.
18. When something is missing in a range of points: it is called a...,
collective outlier.
,19. The whiskers on a box plot extend to...: the 10th and 90th percentiles
(or 5th and 95th)
20. Why are hypothesis tests generally not sufficient for change
detection?: They are slow to detect changes.
21. In CUSUM, T is _____ and C is _____.,: Threshold and a "bring down
factor"
22. In a CUSUM model, you adjust T and C to manage the tradeoff
between...,-
: early detection and false-alarms
23. In exponential smoothing, if the data is less random, then you want
to pick an alpha that is...,: Close to 1.
24 What is the initial condition for T in exponential smoothing with
trending?: T_i=0
25. In cyclic exponential smoothing, L represents...,: The length of the
cycle or season
26. In cyclic exponential smoothing, C_1 ... C_L = ___?,: 1. In other
words, initialize it to no initial cycle.
, 27. Exponential, trending and cyclic smoothing are also referred to as:
single double and triple.
28. Triple smoothing is also known as?: Winter's or Holt-Winter's 29.
What is the optimization formula for Exponential Smoothing?: min(F_t-
Xt)^2 where alpha and beta are between 0 and 1.
30. ARIMA stands for?: Autoregressive Integrated Moving Average
31. Exponential smoothing is an order ___ autoregressive model.:
Infinity. It uses data going all the way back.
32. For ARIMA, the D parameter is used to specify ___.: , The order, or
the differences of the differences of the differences (d-times.)
33. For ARIMA, the P parameters is used to specify ____.,: The order of
periods (autoregression).
34. For ARIMA, the Q parameter is used to specify ______.,: The order
of the moving average.
35. ARIMA(0,1,1) is ?,: Exponential smoothing.
36. What is the order of the ARIMA parameters?: p d q
37. GARCH estimates what?: Variance.
Update ) Intro to Analytics
Modeling | Questions & Answers | Grade A |
100% Correct. Georgia Tech
1. What does SVM stand for?: Support Vector Machine
2. Is written text structured or unstructured?: Unstructured
3. When we increase the sum of the square of the coefficients we...:
Decrease the distance between the lines
4. In SVM soft classifier we tradeoff between maximizing ___ and
minimizing ___: margin and errors
5. If lambda gets small what gets emphasized, large margin or
minimizing training error?,: Minimizing errors.
6. What is a support vector?: A point that holds up a shape.
7. Does ...[(Ta-1)+1/3(a+1)] move an SVM classifier up or down?: Up
8. How do you make errors more costly in a soft SVM classifier?:
include a multiplier for the point-error term.
1
,9. If an SVM coefficient is very close to zero...: that term is not very
important to the classification.
10. What is the difference between standardization and scaling?: Scaling
is bounded in range. Standardization is scaling to a normal distribution.
Standardization is the (value - factor mean) / (factor standard deviation)
11. What is the 2-norm?: Euclidean distance
12. What is the 1-norm?: The rectilinear (Manhattan) distance
13. What is the infinity norm?: The value of the largest dimension
14. Measuring the quality of a model is called?: Validation
15. What does a confusion matrix show?: The performance of a
classification model.
16. A time series outlier that seems "off the curve" is called a...:
contextual outlier.
17. A data element that is different from all other data in a set is called
a...: point outlier.
18. When something is missing in a range of points: it is called a...,
collective outlier.
,19. The whiskers on a box plot extend to...: the 10th and 90th percentiles
(or 5th and 95th)
20. Why are hypothesis tests generally not sufficient for change
detection?: They are slow to detect changes.
21. In CUSUM, T is _____ and C is _____.,: Threshold and a "bring down
factor"
22. In a CUSUM model, you adjust T and C to manage the tradeoff
between...,-
: early detection and false-alarms
23. In exponential smoothing, if the data is less random, then you want
to pick an alpha that is...,: Close to 1.
24 What is the initial condition for T in exponential smoothing with
trending?: T_i=0
25. In cyclic exponential smoothing, L represents...,: The length of the
cycle or season
26. In cyclic exponential smoothing, C_1 ... C_L = ___?,: 1. In other
words, initialize it to no initial cycle.
, 27. Exponential, trending and cyclic smoothing are also referred to as:
single double and triple.
28. Triple smoothing is also known as?: Winter's or Holt-Winter's 29.
What is the optimization formula for Exponential Smoothing?: min(F_t-
Xt)^2 where alpha and beta are between 0 and 1.
30. ARIMA stands for?: Autoregressive Integrated Moving Average
31. Exponential smoothing is an order ___ autoregressive model.:
Infinity. It uses data going all the way back.
32. For ARIMA, the D parameter is used to specify ___.: , The order, or
the differences of the differences of the differences (d-times.)
33. For ARIMA, the P parameters is used to specify ____.,: The order of
periods (autoregression).
34. For ARIMA, the Q parameter is used to specify ______.,: The order
of the moving average.
35. ARIMA(0,1,1) is ?,: Exponential smoothing.
36. What is the order of the ARIMA parameters?: p d q
37. GARCH estimates what?: Variance.
