Coursera Answers

Stanford University Statistical Learning Quiz Answer | Classification

Stanford University Statistical Learning Quiz Answer  Classification

Stanford University Statistical Learning Quiz Answer | Classification


In this article i am gone to share Stanford University Statistical Learning Quiz Answer | Classification with you..


Introduction to Classification Problem Quiz

4.1 R1

Which of the following is the best example of a Qualitative Variable?
  • Height
  • Age
  • Speed
  • Color

4.1 R2

Judging from the plots on page 2 of the notes, which should be the better predictor of Default: Income or Balance?
  • Income.
  • Balance.
  • Both are equally good.
  • Not enough information is given to decide.
Logistic Regression Quiz

4.2.R1

Using the model on page 8 of the notes, what value of Balance will give a predicted Default rate of 50%? (within 3 units of accuracy)
Enter the value of Balance below:
  • 1936.6
Multivariate Logistic Regression Quiz

4.3.R1

Suppose we collect data for a group of students in a statistics class with variables X_1 hours studied, X_2 undergrad GPA, and Y= receive an A. We fit a logistic regression and produce estimated coefficients hatbeta_o = -6, hatbeta_1 = 0.05, hatbeta_2 = 1.
Estimate the probability that a student who studies for 40h and has an undergrad GPA of 3.5 gets an A in the class (within 0.01 accuracy):
  • 0.3775

4.3.R2

How many hours would that student need to study to have a 50% chance of getting an A in the class?:
  • 50
Logistic Regression – Case-Control Sampling and Multiclass Quiz

4.4 R1

In which of the following problems is Case/Control Sampling LEAST likely to make a positive impact?
  • Predicting a shopper’s gender based on the products they buy
  • Finding predictors for a certain type of cancer
  • Predicting if an email is Spam or Not Spam
Discriminant Analysis Quiz

4.5 R1

Suppose that in Ad Clicks (a problem where you try to model if a user will click on a particular ad) it is well known that the majority of the time an ad is shown it will not be clicked. What is another way of saying that?
  • Ad Clicks have a low Prior Probability
  • Ad Clicks have a high Prior Probability.
  • Ad Clicks have a low Density.
  • Ad Clicks have a high Density.
Gaussian Discriminant Analysis – One Variable Quiz

4.6.R1

Which of the following is NOT a linear function in x:
  • f(x) = a + b^2x
  • The discriminant function from LDA
  • delta_k(x) = xfrac{mu_k}{sigma^2} – frac{mu_k^2}{2sigma^2} +log(pi_k)
  • text{logit}(P(y = 1 | x)) where P(y=1 | x) is as in logistic regression
  • P(y=1 | x) from logistic regression
Gaussian Discriminant Analysis – Many Variables

4.7.R1

Why does Total Error keep going down on the graph on page 34 of the notes, even though the False Negative Rate increases?
  • The False Negative Rate does not affect Total Error.
  • A higher False Negative Rate generally decreases Total Error.
  • Positive responses are so uncommon that their impact on the Total Error is small.
  • All of the above
Quadratic Discriminant Analysis and Naive Bayes Quiz

4.8.R1

Which of the following statements best explains the relationship between Quadratic Discriminant Analysis and naive Bayes with Gaussian distributions in each class?
  • Quadratic Discriminant Analysis is a more flexible class of models than naive Bayes
  • Quadratic Discriminant Analysis is a less flexible class of models than naive Bayes
  • Quadratic Discriminant Analysis is an equivalently flexible class of models to naive Bayes
  • For some problems Quadratic Discriminant Analysis is more flexible than naive Bayes, for others the opposite is true.
Classification in R

4.R.R1

In ch4.R, line 13 is “attach(Smarket).” If that line was omitted from the script, which of the following lines would cause an error?:
  • line 15: mean(glm.pred==Direction)
  • line 18: glm.fit = glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family=binomial, subset=train)
  • line 22: Direction.2005=Smarket$Direction[!train]
  • line 30: table(glm.pred,Direction.2005)

Chapter 4 Quiz

4.Q.1

Which of the following tools would be well suited for predicting if a student will get an A in a class based on the student’s height, and parents’ income? Select all that apply:
  • Linear Discriminant Analysis
  • Linear Regression
  • Logistic Regression
  • Random Guess