ISYE 6501
Classification 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
Classification 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
