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

In the first course of the Machine Learning Specialization, you will:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.

In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

### Supervised Machine Learning: Regression and Classification Quiz Answers

### Week 1 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 w*w* after one update step?

- It is not possible to tell if w
*w*will increase or decrease. - w
*w*increases. - w
*w*stays the same - w
*w*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*−*αm*1*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*−*αm*1*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).

Enter answer here

Q2. Which of the following are 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 compute 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 alphaalpha.

- 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 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 one 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 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) does 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

Q2. For the simplified loss function, if the label y^{(i)}=0*y*(*i*)=0, then what does this expression simplify to?

- \log(1-f_{\mathbf{\vec{w}},b}(\mathbf{x}^{(i)})) + log(1-f_{\mathbf{\vec{w}},b}(\mathbf{x}^{(i)}))log(1−
*f***w**,*b*(**x**(*i*)))+*log*(1−*f***w**,*b*(**x**(*i*))) - \log(f_{\vec{w},b}(\mathbf{x}^{(i)})log(
*fw*,*b*(**x**(*i*)) - – \log(1-f_{\mathbf{\vec{w}},b}(\mathbf{x}^{(i)})) – log(1-f_{\mathbf{\vec{w}},b}(\mathbf{x}^{(i)}))−log(1−
*f***w**,*b*(**x**(*i*)))−*log*(1−*f***w**,*b*(**x**(*i*))) - -\log(1-f_{\mathbf{\vec{w}},b}(\mathbf{x}^{(i)}))−log(1−
*f***w**,*b*(**x**(*i*)))

#### 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 high bias (underfit). Thus, adding data is likely to help
- The model has high variance (overfit). Thus, adding data is likely to help
- The model has high variance (overfit). Thus, adding data is, by itself, unlikely to help much.
- The model has 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 \lambda*λ*, what do you expect to happen to the parameters w_1,w_2,…,w_n*w*1,*w*2,…,*wn*?

- This will increase the size of the parameters w_1,w_2,…, w_n
*w*1,*w*2,…,*wn* - This will reduce the size of the parameters w_1,w_2,…, w_n
*w*1,*w*2,…,*wn*

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