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ISYE 6501
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Exam of 8 pages for the course Capa Test Questions at Capa Test Questions (ISYE 6501)
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ISYE 6501Classification problems are commonly solved using what model(s)? - ANS-Support
Vector Machine
Clustering problems are commonly solved using what model(s)? - ANS-k-means
Response Prediction questions are commonly solved using what model(s)? -
ANS--ARIMA
-CART
-Exponential smoothing
-linear regression
-logistic regression
-Random Forest
Validation questions are commonly solved using what model(s)? - ANS--Cross
Validation
Variance Estimation questions are commonly solved using what model(s)? -
ANS--GARCH
Examples of models that are designed for use with time series data - ANS--ARIMA
-CUSUM
-Exponential Smoothing
-GARCH
In the soft classification SVM model where we select coefficients a_0 ... a_m to
minimize sum(max(0, 1 - (sum(a_i * x_ij) + a_0 ) * y_j ) + C * sum(a_i ^ 2) ). If we want
to have a larger margin even though it means possibly having more classification error,
the value of C should get: - ANS-Larger
Best way to split data - ANS--70% for training
-15% for validation
-15% for test
Purpose of a test set - ANS-Estimate quality of selected model
Purpose of a training set - ANS-Fit parameters of all models
, Purpose of a validation set - ANS-compare all models and select best
True or False: The most useful classification models are the ones that correctly classify
the highest fraction of data points. - ANS-False.
Explanation: Sometimes the cost of a false positive is so high that it's worth accepting
more false negatives, or vice versa. Lesson 10.6
A model is built to determine whether data points belong to a category or not. A "true
negative" result is: - ANS-A data point that is not in the category, and the model
correctly says so.
Explanation: True' and 'false' refer to whether the model is correct or not, and 'positive'
and 'negative' refer to whether the model says the point is in the category. Lesson 10.5
A logistic regression model can be especially useful when the response... - ANS-is
binary (zero or one) or is a probability (a number between zero and one). Lesson 10.4
True or False: When using a random forest model, it's easy to interpret how its results
are determined. - ANS-False
Explanation: Unlike a model like regression where we can show the result as a simple
linear combination of each attribute times its regression coefficient, in a random forest
model there are so many different trees used simultaneously that it's difficult to interpret
exactly how any factor or factors affect the result. Lesson 10.3
A common rule of thumb is to stop branching if a leaf would contain less than 5% of the
data points. Why not keep branching and allow models to find very close fits to each
very small subset of data? - ANS-Fitting to very small subsets of data will cause
overfitting.
Explanation: With too few data points, the models will fit to random patterns as well as
real ones. Lesson 10.2
True or false: In a regression tree, every leaf of the tree has a different regression model
that might use different attributes, have different coefficients, etc. - ANS-True.
Explanation: Each leaf's individual model is tailored to the subset of data points that
follow all of the branches leading to the leaf. Lesson 10.1
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