Supervised Machine Learning: Regression and Classification Quiz Answers

Get All Weeks Supervised Machine Learning: Regression and Classification Quiz Answers

Supervised Machine Learning: Regression and Classification Week 01 Quiz Answers

Quiz 1: Supervised vs. unsupervised learning Quiz Answers

Q1. Which are the two common types of supervised learning? (Choose two)

• Regression
• Classification
• Clustering

Q2. Which of these is a type of unsupervised learning?

• Clustering
• Classification
• Regression

Quiz 2:  Regression Quiz Answers

Q1. For linear regression, the model is f_{w,b}(x) = wx + bf

Which of the following are the inputs, or features, that are fed into the model and with which the model is expected to make a prediction?

• xx
• mm
• ww and bb.
• (x,y)(x,y)

Q2. For linear regression, if you find parameters ww and bb so that J(w,b)J(w,b) is very close to zero, what can you conclude?

• This is never possible — there must be a bug in the code.
• The selected values of the parameters ww and bb cause the algorithm to fit the training set really well.
• The selected values of the parameters ww and bb cause the algorithm to fit the training set really poorly.

Quiz 3: Train the model with gradient descent Quiz Answers

Q1. Gradient descent is an algorithm for finding values of parameters w and b that minimize the cost function J.

When \frac{\partial J(w,b)}{\partial w}∂w∂J(w,b)​ is a negative number (less than zero), what happens to ww after one update step?

• It is not possible to tell if ww will increase or decrease.
• ww increases.
• ww stays the same
• ww decreases

Q2. For linear regression, what is the update step for parameter b?

• b = b – \alpha \frac{1}{m} \sum\limits_{i=1}^{m} (f_{w,b}(x^{(i)}) – y^{(i)})x^{(i)}b=b−αm1​i=1∑m​(fw,b​(x(i))−y(i))x(i)
• b = b – \alpha \frac{1}{m} \sum\limits_{i=1}^{m} (f_{w,b}(x^{(i)}) – y^{(i)})b=b−αm1​i=1∑m​(fw,b​(x(i))−y(i))

Week 2 Quiz Answers

Quiz 1: Multiple Linear Regression Quiz Answers

Q1. In the training set below, what is x_4^{(3)}x ? Please type in the number below (this is an integer such as 123, no decimal points).

Answer: 125

Q2. Which of the following are the potential benefits of vectorization? Please choose the best option.

• It makes your code run faster
• It can make your code shorter
• It allows your code to run more easily on parallel computing hardware
• All of the above

Q3. True/False? To make gradient descent converge about twice as fast, a technique that almost always works is to double the learning rate alpha alpha.

• False
• True

Quiz 2: Gradient descent in practice Quiz Answers

Q1. Which of the following is a valid step used during feature scaling?

• Subtract the mean (average) from each value and then divide by the (max – min).
• Add the mean (average) from each value and then divide by the (max – min).

Q2. Suppose a friend ran gradient descent three separate times with three choices of the learning rate \alphaα and plotted the learning curves for each (cost J for each iteration).

For which case, A or B, was the learning rate \alphaα likely too large?

• Both Cases A and B
• case B only
• case A only
• Neither Case A nor B

Q3. Of the circumstances below, for which is feature scaling particularly helpful?

• Feature scaling is helpful when one feature is much larger (or smaller) than another feature.
• Feature scaling is helpful when all the features in the original data (before scaling is applied) range from 0 to 1.

Q4. You are helping a grocery store predict its revenue, and have data on its items sold per week and price per item. What could be a useful engineered feature?

• For each product, calculate the number of items sold times the price per item.
• For each product, calculate the number of items sold divided by the price per item.

Q5. True/False? With polynomial regression, the predicted values f_w,b(x) do not necessarily have to be a straight line (or linear) function of the input feature x.

• True
• False

Week 3 Quiz Answers

Quiz 1: Classification with Logistic Regression Quiz Answers

Q1. Which is an example of a classification task?

• Based on a patient’s blood pressure, determine how much blood pressure medication (a dosage measured in milligrams) the patient should be prescribed.
• Based on the size of each tumor, determine if each tumor is malignant (cancerous) or not.
• Based on a patient’s age and blood pressure, determine how much blood pressure medication (measured in milligrams) the patient should be prescribed.

Q2. Recall the sigmoid function is g(z) = \frac{1}{1+e^{-z}}g(z)=
1+e
−z

1 If z is a large positive number, then:

• g(z)g(z) will be near zero (0)
• g(z)g(z) is near negative one (-1)
• g(z)g(z) will be near 0.5
• g(z)g(z) is near one (1)

Q3. A cat photo classification model predicts 1 if it’s a cat, and 0 if it’s not a cat. For a particular photograph, the logistic regression model outputs g(z)g(z) (a number between 0 and 1). Which of these would be a reasonable criteria to decide whether to predict if it’s a cat?

• Predict it is a cat if g(z) < 0.7
• Predict it is a cat if g(z) = 0.5
• Predict it is a cat if g(z) < 0.5
• Predict it is a cat if g(z) >= 0.5

Q4. True/False? No matter what features you use (including if you use polynomial features), the decision boundary learned by logistic regression will be a linear decision boundary.

• False
• True

Quiz 2: Cost function for logistic regression Quiz Answers

Q1. In this lecture series, “cost” and “loss” have distinct meanings. Which one applies to a single training example?

• Loss
• Cost
• Both Loss and Cost
• Neither Loss nor Cost

Quiz 3: Gradient descent for logistic regression

Q1. Which of the following two statements is a more accurate statement about gradient descent for logistic regression?

• The update steps look like the update steps for linear regression, but the definition of f_{\vec{w},b}(\mathbf{x}^{(i)})fw,b​(x(i)) is different.
• The update steps are identical to the update steps for linear regression.

Quiz 4: The problem of overfitting

Q1. Which of the following can address overfitting?

• Remove a random set of training examples
• Collect more training data
• Select a subset of the more relevant features.
• Apply regularization

Q2. You fit logistic regression with polynomial features to a dataset, and your model looks like this.

What would you conclude? (Pick one)

• The model has a high bias (underfit). Thus, adding data is likely to help
• The model has a high variance (overfit). Thus, adding data is likely to help
• The model has a high variance (overfit). Thus, adding data is, by itself, unlikely to help much.
• The model has a high bias (underfit). Thus, adding data is, by itself, unlikely to help much.

Q3. Suppose you have a regularized linear regression model.  If you increase the regularization parameter \lambdaλ, what do you expect to happen to the parameters w_1,w_2,…,w_nw1​,w2​,…,wn​?

• This will increase the size of the parameters w_1,w_2,…, w_nw1​,w2​,…,wn​
• This will reduce the size of the parameters w_1,w_2,…, w_nw1​,w2​,…,wn​ 

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