1. Ungraded quiz
Unit 6

2. Show me your fingers
Do not shout out the answer, or your classmates will follow what you said.
Use your fingers
One finger (the right finger) = A
Two fingers = B
Three fingers = C
Four fingers = D
No finger = I don’t know. I didn’t study

3. Which of the following statements about resampling is true?
Originally resampling was developed for big data sets.
Two resampling methods, Jackknife and exact tests, are commonly used in big data analytics.
The root of resampling is Monte Carlo simulation.
Resampling does not need real data.

4. Which of the following statements is true?
Like the sampling distribution, the empirical distribution generated by bootstrapping is always normal.
Jackknife does not assume that every observation is treated equally.
Bootstrapping means one sample gives rise to many samples.
Fisher used the exact to solve the problem of lady’s tasting coffee.

5. Which of the following is true?
When the bias is high, the model is unstable.
When the model is simple, the variance is high.
Bias is the distance between the actual and the estimated values.
Bias results from random noise.

6. Which of the following is true?
Bagging reduces bias.
Boosting reduces variance.
Ensemble methods cancel out errors by resampling.
A high-variance model is an over-fitted model.

7. Which of the following is true?
In JMP bootstrap forest randomly subsets the sample and randomly subsets the predictors.
The term “random forest” is trademarked by the inventor and thus no other software can use this name.
In IBM SPSS Modeler random forest is called bootstrap tree.

8. What is the typical portion of the out of bag sample (OOBS)?
25%
30%-33%
50%
75%

9. What is the difference between bagging and boosting?
Bagging is sequential ad boosting is a 2-step process.
Bagging uses sampling without replacement whereas boosting uses sampling with replacement.
In bagging each model is independent but in boosting the previous model informs the next one.
Boosting uses weighted average to create a converged model.

10. What of the following is true?
In bagging and boosting the contribution of the variable is measured by the number of splits and the portion.
When the DV is binary, we should use average predicted values.
When the DV is continuous, we should use majority voting.
When the DV is continuous, we report G2.

11. Which of the following statement is a criterion for model comparison?
Entropy R2
RMSE
AUC
Misclassification rate
All of the above