Machine Learning Foundations: A Case Study Approach Quiz Answer

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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. 

Answer: Click here

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?

Model indexParameters (intercept, slope)Residual sum of squares (RSS)
1(0,1.4)20.51
2(3.1,1.4)15.23
3(2.7, 1.9)13.67
4(0, 2.3)18.99
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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
Machine Learning Foundations: A Case Study Approach Quiz Answer

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

Machine Learning Foundations: A Case Study Approach Quiz Answer

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?

Answer:

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?

Answer:

Machine Learning Foundations: A Case Study Approach Quiz Answer

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.

Week 4: Machine Learning Foundations: A Case Study Approach Quiz Answer

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.)

Answer

Machine Learning Foundations: A Case Study Approach Quiz Answer

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?

User IDFeature vector
1(1.73, 0.01, 5.22)
2(0.03, 4.41, 2.05)
3(1.13, 0.89, 3.76)
Product IDFeature vector
1(3.29, 3.44, 3.67)
2(0.82, 9.71, 3.88)
3(8.34, 1.72, 0.02)
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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)

Answer:

Machine Learning Foundations: A Case Study Approach Quiz Answer

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])?

Answer:

Machine Learning Foundations: A Case Study Approach Quiz Answer

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])?

Answer:

Machine Learning Foundations: A Case Study Approach Quiz Answer

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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