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

## Table of Contents

### 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)

Classification

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

View#### 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?

ViewQ2. 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?

View#### 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?

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

View### 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).

ViewQ2. Which of the following are the potential benefits of vectorization? Please choose the best option.

ViewIt 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.

View#### Quiz 2: Gradient descent in practice Quiz Answers

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

ViewQ2. 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?

ViewQ3. Of the circumstances below, for which is feature scaling particularly helpful?

ViewQ4. 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?

ViewQ5. 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.

View### Week 3 Quiz Answers

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

Q1. Which is an example of a classification task?

ViewQ2. 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:

ViewQ3. 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?

ViewQ4. 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.

View#### 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?

View#### 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?

View#### Quiz 4: The problem of overfitting

Q1. Which of the following can address overfitting?

ViewCollect 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)

ViewQ3. 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*?

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

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