DATA MINING AND STAT LEARN ACTUAL EXAM TEST PAPER FULL QUESTIONS CORRECT
RESPONSES
Question:
SVM Pros/Cons.
Answer:
Pros: It works really well with a clear margin of separation It is effective in high dimensional
spaces. It is effective in cases where the number of dimensions is greater than the number of
samples. It uses a subset of training points in the decision function (called support vectors), so it is
also memory efficient. Cons: Not good for very large data sets Not good for when the data set has
more noise i.e. target classes are overlapping Doesn't directly provide probability estimates.
Question:
K-nearest neighbor (K-NN).
Answer:
An unsupervised classification algorithm. Looks at the X number of closest points to the new one
and classifies as whichever is most common.
Question:
K-nearest neighbor (K-NN) Pros/Cons.
Answer:
Pros: No assumptions about data Easy to understand/Interpret Varsatile Cons: Computationally
expensive because algorithm stores all training data Sensitive to irrelevant features and scale of data
Question:
k-fold cross validation.
Answer:
,Validation Technique where data is divided into X number of data subsets. Each subset is then used
as a for testing while the rest are used for training. The algorithm then rotates through each subset
and averages the results
Question:
K Fold cross Validation Pros/Cons.
Answer:
Pros: Validates Performance of model Can create balance across predicted features classes Cons:
Doesn't work well with time series data The aggregate scores of your model could miss some
important extreme values or overpower them so theyre harder to pick up on
Question:
k-means clustering.
Answer:
Unsupervised learning heuristic that sets x starts by assigning x number of cluster centers, then
clusters all data points into each of them based on distance. The center point of each cluster is then
calculated and all data points are again re clustered. Repeat process until no-data points change
clusters. Ideal number of clusters can be identified via elbow diagram.
Question:
k-means pros and cons.
Answer:
Pros: Simple to implement Scales well to large data sets Easily adaptable Cons: Choosing K
manually can bias it towards initial values sensitive to outliers
Question:
Grubbs Outlier Test.
Answer:
A formula that uses an outlier's value, the mean of the data, and the standard deviation to determine
whether or not the data point is within the confidence interval for a normal distribution or should be
, thrown out
Question:
CUSUM.
Answer:
Change detection model that keeps a running total of the amount that observations vary above the
expected value. The running total exceeds a preset threshold value, it indicates there has been a
change.
Question:
CUSUM Pros/Cons.
Answer:
Pros: Best way to detect the small shifts of process mean especially 0.5 to 2 SD from the target mean
Easy to identify visually the shifts in process mean Cons: Cumbersome to establish and maintain
Tough to interpret the patterns. Choosing C and T values is a pro and con as it can cause bias but
creates more flexibility
Question:
Exponential Smoothing.
Answer:
Technique regarding time series data in which older observations are assigned exponentially
decreasing weights, so more emphasis is given to recent observations. Can include trends,
seasonality, and cyclic patterns to account for expected differences in observations over time
Question:
Exponential Smoothing Pros and Cons.
Answer:
Pros: Easy to learn and apply Can produce accurate forecasts can account for
trends/seasonality/cyclic effects Works well when mean/variance/etc are expected to remain
relatively constant Cons: Forecasts can sometimes have lag
RESPONSES
Question:
SVM Pros/Cons.
Answer:
Pros: It works really well with a clear margin of separation It is effective in high dimensional
spaces. It is effective in cases where the number of dimensions is greater than the number of
samples. It uses a subset of training points in the decision function (called support vectors), so it is
also memory efficient. Cons: Not good for very large data sets Not good for when the data set has
more noise i.e. target classes are overlapping Doesn't directly provide probability estimates.
Question:
K-nearest neighbor (K-NN).
Answer:
An unsupervised classification algorithm. Looks at the X number of closest points to the new one
and classifies as whichever is most common.
Question:
K-nearest neighbor (K-NN) Pros/Cons.
Answer:
Pros: No assumptions about data Easy to understand/Interpret Varsatile Cons: Computationally
expensive because algorithm stores all training data Sensitive to irrelevant features and scale of data
Question:
k-fold cross validation.
Answer:
,Validation Technique where data is divided into X number of data subsets. Each subset is then used
as a for testing while the rest are used for training. The algorithm then rotates through each subset
and averages the results
Question:
K Fold cross Validation Pros/Cons.
Answer:
Pros: Validates Performance of model Can create balance across predicted features classes Cons:
Doesn't work well with time series data The aggregate scores of your model could miss some
important extreme values or overpower them so theyre harder to pick up on
Question:
k-means clustering.
Answer:
Unsupervised learning heuristic that sets x starts by assigning x number of cluster centers, then
clusters all data points into each of them based on distance. The center point of each cluster is then
calculated and all data points are again re clustered. Repeat process until no-data points change
clusters. Ideal number of clusters can be identified via elbow diagram.
Question:
k-means pros and cons.
Answer:
Pros: Simple to implement Scales well to large data sets Easily adaptable Cons: Choosing K
manually can bias it towards initial values sensitive to outliers
Question:
Grubbs Outlier Test.
Answer:
A formula that uses an outlier's value, the mean of the data, and the standard deviation to determine
whether or not the data point is within the confidence interval for a normal distribution or should be
, thrown out
Question:
CUSUM.
Answer:
Change detection model that keeps a running total of the amount that observations vary above the
expected value. The running total exceeds a preset threshold value, it indicates there has been a
change.
Question:
CUSUM Pros/Cons.
Answer:
Pros: Best way to detect the small shifts of process mean especially 0.5 to 2 SD from the target mean
Easy to identify visually the shifts in process mean Cons: Cumbersome to establish and maintain
Tough to interpret the patterns. Choosing C and T values is a pro and con as it can cause bias but
creates more flexibility
Question:
Exponential Smoothing.
Answer:
Technique regarding time series data in which older observations are assigned exponentially
decreasing weights, so more emphasis is given to recent observations. Can include trends,
seasonality, and cyclic patterns to account for expected differences in observations over time
Question:
Exponential Smoothing Pros and Cons.
Answer:
Pros: Easy to learn and apply Can produce accurate forecasts can account for
trends/seasonality/cyclic effects Works well when mean/variance/etc are expected to remain
relatively constant Cons: Forecasts can sometimes have lag
