# 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)

### 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