# 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)

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Regression

Classification

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

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Clustering

#### 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?

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xx

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?

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The selected values of the parameters ww and bb cause the algorithm to fit the training set really well.

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}∂wJ(w,b)​ is a negative number (less than zero), what happens to ww after one update step?

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ww decreases

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

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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)

#### 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).

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125

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

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It makes your code run faster

It can make your code shorter

It allows your code to run more easily on parallel computing hardware

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.

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False

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

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Subtract 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?

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case B only

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

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Feature scaling is helpful when one feature is much larger (or smaller) than another feature.

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?

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For each product, calculate the number of items sold times 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.

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True

#### Quiz 1: Classification with Logistic Regression Quiz Answers

Q1. Which is an example of a classification task?

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Based on the size of each tumor, determine if each tumor is malignant (cancerous) or not.

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:

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g(z)g(z) will be near zero (0)

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?

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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.

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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?

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Loss

#### 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?

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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.

#### Quiz 4: The problem of overfitting

Q1. Which of the following can address overfitting?

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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)

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The model has a high variance (overfit). 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​?

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

#### Get All Course Quiz Answers of Machine Learning Specialization

Supervised Machine Learning: Regression and Classification Quiz Answers