# Machine Learning Foundations: A Case Study Approach Quiz Answer

## Get All Weeks Machine Learning Foundations: A Case Study Approach Quiz Answers

### Week 1: Machine Learning Foundations: A Case Study Approach Quiz Answer

#### Quiz 1: S Frames

Q 1:Download the Wiki People SFrame. Then open a new Jupyter notebook, import TuriCreate, and read the SFrame data.

Q 2: How many rows are in the SFrame? (Do NOT use commas or periods.)

Q 3: Which name is in the last row?

• C​thy Caruth
• F​awaz Damrah

Q 4: Read the text column for Harpdog Brown. He was honored with:

• A​ Grammy award for his latest blues album.
• A gold harmonica to recognize his innovative playing style.
• A lifetime membership in the Hamilton Blues Society.

Q 5: Sort the SFrame according to the text column, in ascending order. What is the name entry in the first row?

• Z​ygfryd Szo
• D​igby Morrell
• 0​07 James Bond
• 108 (artist)
• 8​ Ball Aitken

### Week 2: Machine Learning Foundations: A Case Study Approach Quiz Answer

#### Quiz 1: Regression

Q 2: True or false: The model that best minimizes training error is the one that will perform best for the task of prediction on new data.

• True
• False

Q 3: The following table illustrates the results of evaluating 4 models with different parameter choices on some data set. Which of the following models fits this data the best?

• Model 1
• Model 2
• Model 3
• Model 4

Q 4: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

• w0
• w1
• w2
• none of the above

Q 5: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

• w0
• w1
• w2
• none of the above

Q 6: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

• w0
• w1
• w2
• none of the above

Q 7: Assume we fit the following quadratic function: f(x) = w0+w1*x+w2*(x^2) to the dataset shown (blue circles). The fitted function is shown by the green curve in the picture below. Out of the 3 parameters of the fitted function (w0, w1, w2), which ones are estimated to be 0? (Note: you must select all parameters estimated as 0 to get the question correct.)

• w0
• w1
• w2
• none of the above

Q 8: Which of the following plots would you not expect to see as a plot of training and test error curves?

Q 9: True or false: One always prefers to use a model with more features since it better captures the true underlying process.

• True
• False

#### Quiz 2: Predicting house prices

Q 1: Selection and summary statistics: We found the zip code with the highest average house price. What is the average house price of that zip code?

• \$75,000
• \$7,700,000
• \$540,088
• \$2,160,607

Q 2: Filtering data: What fraction of the houses have living space between 2000 sq.ft. and 4000 sq.ft.?

• Between 0.2 and 0.29
• Between 0.3 and 0.39
• Between 0.4 and 0.49
• Between 0.5 and 0.59
• Between 0.6 and 0.69

Q 3: Building a regression model with several more features: What is the difference in RMSE between the model trained with my_features and the one trained with advanced_features?

• the RMSE of the model with advanced_features lower by less than \$25,000
• the RMSE of the model with advanced_features lower by between \$25,001 and \$35,000
• the RMSE of the model with advanced_features lower by between \$35,001 and \$45,000
• the RMSE of the model with advanced_features lower by between \$45,001 and \$55,000
• the RMSE of the model with advanced_features lower by more than \$55,000

### Week 3: Machine Learning Foundations: A Case Study Approach Quiz Answer

#### Quiz 1: Classification

Q 1: The simple threshold classifier for sentiment analysis described in the video (check all that apply):

• Must have pre-defined positive and negative attributes
• Must either count attributes equally or pre-define weights on attributes
• Defines a possibly non-linear decision boundary

Q 2: For a linear classifier classifying between “positive” and “negative” sentiment in a review x, Score(x) = 0 implies (check all that apply):

• The review is very clearly “negative”
• We are uncertain whether the review is “positive” or “negative”
• We need to retrain our classifier because an error has occurred

Q 3: For which of the following datasets would a linear classifier perform perfectly?

Q 4: True or false: High classification accuracy always indicates a good classifier.

• True
• False

Q 5: True or false: For a classifier classifying between 5 classes, there always exists a classifier with an accuracy greater than 0.18.

• True
• False

Q 6: True or false: A false negative is always worse than a false positive.

• True
• False

Q 7: Which of the following statements are true? (Check all that apply)

• Test error tends to decrease with more training data until a point, and then does not change (i.e., curve flattens out)
• Test error always goes to 0 with an unboundedly large training dataset
• Test error is never a function of the amount of training data

#### Quiz 2: Analyzing product sentiment

Q 1: Out of the 11 words in selected_words, which one is most used in the reviews in the dataset?

• awesome
• love
• hate
• great

Q 2: Out of the 11 words in selected_words, which one is least used in the reviews in the dataset?

• wow
• amazing
• terrible
• awful
• love

Q 3: Out of the 11 words in selected_words, which one got the most positive weight in the selected_words_model?

(Tip: when printing the list of coefficients, make sure to use print_rows(rows=12) to print ALL coefficients.)

• amazing
• awesome
• love
• fantastic
• terrible

Question 4: Out of the 11 words in selected_words, which one got the most negative weight in the selected_words_model?

(Tip: when printing the list of coefficients, make sure to use print_rows(rows=12) to print ALL coefficients.)

• horrible
• terrible
• awful
• hate
• love

Q 5: Which of the following ranges contains the accuracy of the selected_words_model on the test_data?

• 0.811 to 0.841
• 0.841 to 0.871
• 0.871 to 0.901
• 0.901 to 0.931

Q 6: Which of the following ranges contains the accuracy of the sentiment_model in the IPython Notebook from lecture on the test_data?

• 0.811 to 0.841
• 0.841 to 0.871
• 0.871 to 0.901
• 0.901 to 0.931

Q 7: Which of the following ranges contains the accuracy of the majority class classifier, which simply predicts the majority class on the test_data?

• 0.811 to 0.843
• 0.843 to 0.871
• 0.871 to 0.901
• 0.901 to 0.931

Q 8: How do you compare the different learned models with the baseline approach where we are just predicting the majority class?

• They all performed about the same.
• The model learned using all words performed much better than the one using the only the selected_words. And, the model learned using the selected_words performed much better than just predicting the majority class.
• The model learned using all words performed much better than the other two. The other two approaches performed about the same.
• Predicting the simply majority class performed much better than the other two models.

Q 9: Which of the following ranges contains the ‘predicted_sentiment’ for the most positive review for ‘Baby Trend Diaper Champ’, according to the sentiment_model from the IPython Notebook from lecture?

• Below 0.7
• 0.7 to 0.8
• 0.8 to 0.9
• 0.9 to 1.0

Q 10: Consider the most positive review for ‘Baby Trend Diaper Champ’ according to the sentiment_model from the IPython Notebook from lecture. Which of the following ranges contains the predicted_sentiment for this review, if we use the selected_words_model to analyze it?

• Below 0.7
• 0.7 to 0.8
• 0.8 to 0.9
• 0.9 to 1.0

Q 11: Why is the value of the predicted_sentiment for the most positive review found using the sentiment_model much more positive than the value predicted using the selected_words_model?

• The sentiment_model is just too positive about everything.
• The selected_words_model is just too negative about everything.
• This review was positive, but used too many of the negative words in selected_words.
• None of the selected words appeared in the text of this review.

#### Quiz 1: Clustering and Similarity

Q 1:A country, called Simpleland, has a language with a small vocabulary of just “the”, “on”, “and”, “go”, “round”, “bus”, and “wheels”. For a word count vector with indices ordered as the words appear above, what is the word count vector for a document that simply says “the wheels on the bus go round and round.”

Please enter the vector of counts as follows: If the counts were [“the”=1, “on”=3, “and”=2, “go”=1, “round”=2, “bus”=1, “wheels”=1], enter 1321211.

Question 2: In Simpleland, a reader is enjoying a document with a representation: [1 3 2 1 2 1 1]. Which of the following articles would you recommend to this reader next?

• [7 0 2 1 0 0 1]
• [1 7 0 0 2 0 1]
• [1 0 0 0 7 1 2]
• [0 2 0 0 7 1 1]

Question 3: A corpus in Simpleland has 99 articles. If you pick one article and perform a 1-nearest neighbor search to find the closest article to this query article, how many times must you compute the similarity between two articles?

• 98
• 98*2 = 196
• 98/2 = 49
• (98)^2
• 99

Question 4: For the TF-IDF representation, does the relative importance of words in a document depend on the base of the logarithm used? For example, take the words “bus” and “wheels” in a particular document. Is the ratio between the TF-IDF values for “bus” and “wheels” different when computed using log base 2 versus log base 10?

• Yes
• No

Question 5:Which of the following statements are true? (Check all that apply):

• Deciding whether an email is spam or not spam using the text of the email and some spam / not spam labels is a supervised learning problem.
• Dividing emails into two groups based on the text of each email is a supervised learning problem.
• If we are performing clustering, we typically assume we either do not have or do not use class labels in training the model.

Question 6: Which of the following pictures represents the best k-means solution? (Squares represent observations, plus signs are cluster centers, and colors indicate assignments of observations to cluster centers.)

#### Quiz 2: Retrieving Wikipedia articles

Q 1: Top word count words for Elton John

• (the, john, singer)
• (england, awards, musician)
• (the, in, and)
• (his, the, since)
• (rock, artists, best)

Question 2: Top TF-IDF words for Elton John

• (furnish,elton,billboard)
• (the,of,has)
• (awards,rock,john)
• (elton,john,singer)

Question 3: The cosine distance between ‘Elton John’s and ‘Victoria Beckham’s articles (represented with TF-IDF) falls within which range?

• 0.1 to 0.29
• 0.3 to 0.49
• 0.5 to 0.69
• 0.7 to 0.89
• 0.9 to 1.0

Question 4: The cosine distance between ‘Elton John’s and ‘Paul McCartney’s articles (represented with TF-IDF) falls within which range?

• 0.1 to 0.29
• 0.3 to 0.49
• 0.5 to 0.69
• 0.7 to 0.89
• 0.9 to 1

Question 5: Who is closer to ‘Elton John’, ‘Victoria Beckham’ or ‘Paul McCartney’?

• Victoria Beckham
• Paul McCartney

Question 6: Who is the nearest cosine-distance neighbor to ‘Elton John’ using raw word counts?

• Billy Joel
• Cliff Richard
• Roger Daltrey
• George Bush

Question 7: Who is the nearest cosine-distance neighbor to ‘Elton John’ using TF-IDF?

• Roger Daltrey
• Rod Stewart
• Tommy Haas
• Elvis Presley

Question 8: Who is the nearest cosine-distance neighbor to ‘Victoria Beckham’ using raw word counts?

• Stephen Dow Beckham
• Louis Molloy
• Mary Fitzgerald (artist)

Question 9: Who is the nearest cosine-distance neighbor to ‘Victoria Beckham’ using TF-IDF?

• Mel B
• Caroline Rush
• David Beckham
• Carrie Reichardt

### Week 5: Machine Learning Foundations: A Case Study Approach Quiz Answer

#### Quiz 1: Recommender Systems

Q1: Recommending items based on global popularity can (check all that apply):

• provide personalization
• capture context (e.g., time of day)
• none of the above

Question 2: Recommending items using a classification approach can (check all that apply):

• provide personalization
• capture context (e.g., time of day)
• none of the above

Question 3:Recommending items using a simple count-based co-occurrence matrix can (check all that apply):

• provide personalization
• capture context (e.g., time of day)
• none of the above

Question 4:Recommending items using featured matrix factorization can (check all that apply):

• provide personalization
• capture context (e.g., time of day)
• none of the above

Question 5:Normalizing co-occurrence matrices is used primarily to account for:

• people who purchased many items
• items purchased by many people
• eliminating rare products
• none of the above

Question 6: A store has 3 customers and 3 products. Below are the learned feature vectors for each user and product. Based on this estimated model, which product would you recommend most highly to User #2?

• Product #1
• Product #2
• Product #3

Question 7: For the liked and recommended items displayed below, calculate the recall and round to 2 decimal points. (As in the lesson, green squares indicate recommended items, and magenta squares are liked items. Items not recommended are grayed out for clarity.) Note: enter your answer in American decimal format (e.g. enter 0.98, not 0,98)

Question 8: For the liked and recommended items displayed below, calculate the precision and round to 2 decimal points. (As in the lesson, green squares indicate recommended items, and magenta squares are liked items. Items not recommended are grayed out for clarity.) Note: enter your answer in American decimal format (e.g. enter 0.98, not 0,98)

Question 9: Based on the precision-recall curves in the figure below, which recommender would you use?

• RecSys #1
• RecSys #2
• RecSys #3

#### Quiz 2: Recommending songs

Question 1: Which of the artists below have had the most unique users listening to their songs?

• Kanye West
• Foo Fighters
• Taylor Swift

Question 2: Which of the artists below is the most popular artist, the one with the highest total listen_count, in the data set?

• Taylor Swift
• Kings of Leon
• Coldplay

Question 3: Which of the artists below is the least popular artist, the one with the smallest total listen_count, in the data set?

• William Tabbert
• Velvet Underground & Nico
• Kanye West
• The Cool Kids

### Week 6: Machine Learning Foundations: A Case Study Approach Quiz Answer

#### Quiz 1: Deep Learning

Question 1: Which of the following statements are true? (Check all that apply)

• Linear classifiers are never useful, because they cannot represent XOR.
• Linear classifiers are useful, because, with enough data, they can represent anything.
• Having good non-linear features can allow us to learn very accurate linear classifiers.
• none of the above

Question 2: A simple linear classifier can represent which of the following functions? (Check all that apply)

• x1 OR x2 OR NOT x3
• x1 AND x2 AND NOT x3
• x1 OR (x2 AND NOT x3)
• none of the above

Question 3: Which of the following neural networks can represent the following function? Select all that apply.

(x1 AND x2) OR (NOT x1 AND NOT x2)

Question 4: Which of the following statements is true? (Check all that apply)

• Features in computer vision act like local detectors.
• Deep learning has had an impact in computer vision because it’s used to combine all the different hand-created features that already exist.
• By learning non-linear features, neural networks have allowed us to automatically learn detectors for computer vision.
• none of the above

Question 5: If you have lots of images of different types of plankton labeled with their species name and lots of computational resources, what would you expect to perform better predictions:

• a deep neural network trained on this data.
• a simple classifier trained on this data, using deep features as input, which were trained using ImageNet data.

Question 6: If you have a few images of different types of plankton labeled with their species name, what would you expect to perform better predictions:

• a deep neural network trained on this data.
• a simple classifier trained on this data, using deep features as input, which were trained using ImageNet data.

#### Quiz 2: Deep features for image retrieval

Question 1: What’s the least common category in the training data?

• bird
• dog
• cat
• automobile

Question 2: Of the images below, which is the nearest ‘cat’ labeled image in the training data to the first image in the test data (image_test[0:1])?

Question 3: Of the images below, which is the nearest ‘dog’ labeled image in the training data to the the first image in the test data (image_test[0:1])?

Question 4: :For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘cat’ in the training data?

• 33 to 35
• 35 to 37
• 37 to 39
• 39 to 41
• Above 41

Question 5: For the first image in the test data, in what range is the mean distance between this image and its 5 nearest neighbors that were labeled ‘dog’ in the training data?

• 33 to 35
• 35 to 37
• 37 to 39
• 39 to 41
• Above 41

Question 6: On average, is the first image in the test data closer to its 5 nearest neighbors in the ‘cat’ data or in the ‘dog’ data?

• cat
• dog

Question 7: In what range is the accuracy of the 1-nearest neighbor classifier at classifying ‘dog’ images from the test set?

• 50 to 60
• 60 to 70
• 70 to 80
• 80 to 90
• 90 to 100
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