Reference no: EM133459828
Questions
1. Cross-validation is used to estimate generalization performance. True or false
2. Adding more complexity to a model will generally increase its performance on the training set. true or false
3. Complex models always give better generalization performance than simple models. true or false
4. A fitting curve plots:
a. True positive rate vs. false positive rate
b. True positive rate vs. false negative rate
c. Generalization performance vs. size of training set
d. Generalization performance vs. model complexity
5. Which is NOT a technique for reducing/avoiding overfitting in tree induction?
a. Choosing branches based on the largest improvement in information gain
b. Stopping tree growth when information becomes unreliable
c. Selecting tree size based on holdout validation
d. Reducing tree size by cutting off branches and replacing them with leaves
6. Which is NOT a benefit of using cross-validation for model induction evaluation?
a. It provides an estimate of generalization performance
b. It provides statistics on estimated performance, so that we can understand how performance will vary across data sets
c. It's always quick to compute relative to other holdout methods
d. It makes better use of limited data by using all data for both training and testing
7. Learning curves:
a. Are used to select an optimal parameter complexity
b. Are equivalent to fitting curves
c. Can illustrate whether obtaining more data would be a good investment
d. Plot true positive rate vs false positive rate
8. More complex models:
a. Always have better predictive performance
b. Tend to overfit more
c. Are easier to train than simpler models
d. Are much easier to interpret compared to simpler models