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ISYE 6501 Midterm 1 Questions with 100% correct answers | verified | latest update 2024
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ISYE 6501 Midterm 1True 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
- Each leaf's individual model is tailored to the subset of data points that follow all of the
branches leading to the leaf.
True or false: Tree-based approaches can be used for other models besides regression.
- ANS-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).
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.
- With too few data points, the models will fit to random patterns as well as real ones.
True or False: When using a random forest model, it's easy to interpret how its results
are determined. - ANS-False
- 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.
A logistic regression model can be especially useful when the response... - ANS-- ...is a
probability (a number between zero and one).
- ...is binary (either zero or one).
- Logistic regressions can be useful for either situation.
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.
- 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.
, True or False: The most useful classification models are the ones that correctly classify
the highest fraction of data points. - ANS-False
- Sometimes the cost of a false positive is so high that it's worth accepting more false
negatives, or vice versa.
PreviousNext
Adjusted R-squared/Adjusted R2 - ANS-Variant of R2 that encourages simpler models
by penalizing the use of too many variables
Akaike information criterion (AIC) - ANS-Model selection technique that trades off
between model fit and model complexity. When comparing models, the model with lower
AIC is preferred. Generally penalizes complexity less than BIC
Algorithm - ANS-Step-by-step procedure designed to carry out a task.
Area under curve/AUC - ANS-Area under the ROC curve; an estimate of the
classification model's accuracy. Also called concordance index
ARIMA - ANS-Autoregressive integrated moving average.
Attribute - ANS-A characteristic or measurement - for example, a person's height or the
color of a car. Generally interchangeable with "feature", and often with "covariate" or
"predictor". In the standard tabular format, a column of data
Autoregression - ANS-Regression technique using past values of time series data as
predictors of future values.
Autoregressive integrated moving average (ARIMA) - ANS-Time series model that uses
differences between observations when data is nonstationary. Also called Box-Jenkins.
Bayes' theorem/Bayes' rule - ANS-Fundamental rule of conditional probability:
𝑃𝑃(𝐴𝐴|𝐵𝐵) = 𝑃𝑃(𝐵𝐵|𝐴𝐴)𝑃𝑃(𝐴𝐴)
Bayesian Information criterion (BIC - ANS-Model selection technique that trades off
model fit and model complexity. When comparing models, the model with lower BIC is
preferred. Generally penalizes complexity more than AIC.
Bayesian regression - ANS-Regression model that incorporates estimates of how
coefficients and error are distributed
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