# 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.)

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59071

Q 3: Which name is in the last row?

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F​awaz Damrah

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

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A​ Grammy award for his latest blues album.

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

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D​igby Morrell

### 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.

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True

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?

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1.Model 1
2.Model 2
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.)<!– wp:shortcode –> View

James P. Grant
<!– /wp:shortcode –>

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w2

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.)

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1.w2
2.w0

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.)

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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.)

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w0

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.

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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?

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\$2,160,607

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

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Between 0.3 and 0.39

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?

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the RMSE of the model with advanced_features lower by between \$35,001 and \$45,000

### 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):

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Must have pre-defined positive and negative attributes

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

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We are uncertain whether the review is “positive” or “negative”

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.

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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.

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True

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

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False

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

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1.Test error is never a function of the amount of training data
2.Test error tends to decrease with more training data until a point, and then does not change (i.e., curve flattens out)

#### 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?

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love

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

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wow

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.)

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Jlove

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.)

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terrible

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

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0.871 to 0.901

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

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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?

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0.811 to 0.843

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

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The model learned using all words performed much better than the other two. The other two approaches performed about the same.

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?

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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?

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0.7 to 0.8

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?

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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.

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21112111

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?

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[1 0 0 0 7 1 2]

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?

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98

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?

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Yes

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

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1.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.
2.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

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(the, john, singer)

Question 2: Top TF-IDF words for Elton John

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(furnish,elton,billboard)

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

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0.5 to 0.69

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

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0.7 to 0.89

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

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Paul McCartney

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

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Roger Daltrey

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

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Rod Stewart

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

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Mary Fitzgerald (artist)

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

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David Beckham

### 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):

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provide personalization

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

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capture context (e.g., time of day)

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

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provide personalization

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

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capture context (e.g., time of day)

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

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items purchased by many people

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?

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Product #2

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)

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0.33

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)

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0.25

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

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RecSys #1

#### Quiz 2: Recommending songs

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

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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?

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Kings of Leon

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

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William Tabbert

### 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)

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Having good non-linear features can allow us to learn very accurate linear classifiers.

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

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x1 OR x2 OR NOT x3

x1 AND x2 AND NOT x3

x1 OR (x2 AND NOT x3)

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)

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Deep learning has had an impact in computer vision because it’s used to combine all the different hand-created features that already exist.

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:

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a deep neural network trained on this 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:

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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?

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bird

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?

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35 to 37

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?

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37 to 39

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?

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cat

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

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60 to 70
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