# 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