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| p. 22, footnote 1 | Quotation marks should start before "Zestimates..." not before "Harney..." |
| p. 22 | The URL for the dataset no longer works. Instead, go to https://data.boston.gov/dataset/property-assessment and choose Property Assessment FY2014 |
| Tables 2.11-2.13 | The regression model should not include TAX as a predictor. See the R code for the correct output. |
| p. 48 box by Herb Edelstein | Copyright year should be 2017 |
| Fig 3.1, R code | Code line: plot(housing.df$MEDV.... , xlab = "MDEV",...) should read: plot(housing.df$MEDV ~ housing.df$LSTAT, xlab = "LSTAT", ylab = "MEDV") |
| Fig 3.9, R code | Name of dataset should be Amtrak.csv (not Amtrak data.csv): Amtrak.df <- read.csv("Amtrak.csv") |
| p.80-81 and Fig 3.14 caption | p.80: Remove the text "Circle size represents the number of transactions that the node (seller or buyer) was involved in within this network. Line width represents the number of auctions that the bidder--seller pair interacted in." Fig 3.14 caption: remove "Circle size represents the node's number of transactions. Line width represents the number of transactions between that pari of seller-buyer" |
| Fig 5.2, R code | Code line: validation <- sample(toyota.corolla.df$Id, 400) should read: validation <- sample(setdiff(toyota.corolla.df$Id, training), 400) |
| p. 126 top | First paragraph should read: "The top-right cell gives the number of class 1 members that were misclassified as 0's... lower-left cell gives the number of class 0 members that were misclassified as 1s (25 such records)." |
| p. 138, Fig 5.6 caption | replace "top" with "left", and "bottom" with "right" |
| Fig 5.7, caption | add: (Note: Percentiles do not match deciles exactly due to the small sample of discrete data, with multiple records sharing the same decile boundary) |
| Fig 5.10, Fig 5.11 | "Classify as 'x'" should be at bottom and "Classify as 'o'" should be at top |
| p. 148, Problem 5.6 |
In problem 5.6, text should read "The global mean is about $2500" In part (b), text should read "roughly double the sales effort" |
| Table 6.2, R code | Commented out text should read: # use lm() to run a linear regression of Price on all the predictors in the # training set (it will automatically turn Fuel_Type into dummies). |
| p. 166 | Paragraph before last: delete "In comparison... any predictor". Instead: "The results for forward selection (Table 6.7) and stepwise selection..." |
| p. 167 Table 6.6 | Ignore comment "set directions..." |
| Table 6.7 | The output is incorrect. It should be identical to the output in Table 6.8. Add code lines: # create model with no predictors car.lm.null <- lm(Price~1, data = train.df) # use step() to run forward regression. car.lm.step <- step(car.lm.null, scope=list(lower=car.lm.null, upper=car.lm), direction = "forward") summary(car.lm.step) # Which variables were added? car.lm.step.pred <- predict(car.lm.step, valid.df) accuracy(car.lm.step.pred, valid.df$Price) |
| p. 169 | Problem 6.1 part (c), ignore the final text "What is the prediction error?" |
| p. 178, 5th line from bottom |
should read "We would choose k=8, which maximizes our accuracy..." |
| p. 184, prob 7.2 (a) |
should read: "Consider the following customer: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education = 2, Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1, and Credit Card = 1. Perform a k-NN classification with all predictors except ID and ZIP code using k = 1. |
| p.185 prob 7.2(d) | should read: "Education = 2" |
| p. 206 | last paragraph (before IF) should read "The values below a white node are the counts of the two classes (0 and 1) in that node" |
| p. 209 | [This is a clarification] Addition: "As with k-nearest-neighbors, a predictor with m categories (m>2) should be factored into m dummies (not m-1). In addition, whether predictors are numerical or categorical, it does not make any difference whether they are standardized (normalized) or not." |
| p. 235-6, Problem 9.3 |
(a): replace 100 with 30. (a)(ii): delete "What is happening with the training set predictions?" (a)(iii): replace text with "How might we achieve better validation predictive performance at the expense of training performance?" (a)(iv): replace text with "Create a less deep tree by leaving the arguments cp, minbucket, and maxdepth at their defaults. Compared to the deeper tree, what is the predictive performance on the validation set?" (b): replace last sentence "Keep the minimum..." with "As in the less deep regression tree, leave the arguments cp, minbucket, and maxdepth at their defaults" (b)(i) and (b)(ii): use the less deep RT for these questions. |
| Ch 10 | Text after eq. (10.5) should be: "a unit increase in predictor xj is associated with an average increase of eβj ×100% in the odds" |
| p. 246 | line 7 should read e(0.03757)(100) instead of e(0.039)(100) |
| p. 251 | Text should read "we see that Sundays and Tuesdays saw the largest proportion of delays" |
| p. 253 | The reference to Figure 10.4 should be to Figure 10.6 (creating base categories) |
| p. 256, R code | Should be gain <- gains(valid.df$isDelay, pred, groups=100) |
| p. 278 | For output node 6 the error is 0.481(1-0.481)(0-0.481)= -0.120 |
| Table 11.6 | Due to a change in caret package for confusionMatrix, make sure to first convert each variable into factors, e.g. confusionMatrix(as.factor(validation.class), as.factor(accidents.df[validation,]$MAX_SEV_IR)) |
| Table 11.7 | Table 11.7 is redundant and should be deleted (the same output appears in Table 11.6) |
| p. 302 | -50.58 should be -51.58 |
| Chap 12 | In equation (12.2) formulas should have square-root |
| Table 13.1 | 3rd line from bottom of the table, code should read "...data = train.df" instead of "...data = bank.df" |
| Table 13.2 | Final confusion matrix (for boosting) should read
Reference |
| p. 302 | In line 2, replace "a sample of 1000 records was drawn" with "a reduced sample of 600 records was drawn (with categories combined so that most predictors are binary)" |
| p. 365 | In Distance Measures for Categorical Data, replace "x_ij's" with "p measurements", and replace n with p in the table and in the Matching coefficient formula. |
| Ch 15-17 | Several of the time series datasets used in the problems (souvenir sales, shampoo sales, Australian wine sales) have a new source reference: Hyndman, R., and Yang, Y. Z. (2018). tsdl: Time Series Data Library. v0.1.0. https://pkg.yangzhourang.com/tsdl/ |
| Problem 15.2.c (p. 383) | Should read: "... with respect to the categorical variables (10 to 12)" |
| p. 391, Fig 16.1 code | Lines 2-3 of commented out text should read: # with monthly data, the frequency of periods per cycle is 12 (per year). # arguments start and end are (cycle [=year] number, seasonal period [=month] number) pairs. |
| p. 420, last para | Reference to Figure 17.6 should be Table 17.7 (AR(1) model) |
| p. 463 | Closeness definition should be: This is measured by finding the shortest path from that node to all the other nodes, then taking the reciprocal of the sum of these path lengths. |
| Table 19.3 | Values for betweenness and closeness should be: > betweenness(g) Dave Jenny Peter John Sam Albert 0 0 6 0 4 0 > closeness(g) Dave Jenny Peter John Sam Albert 0.12500000 0.12500000 0.16666667 0.12500000 0.12500000 0.08333333 |
| Table 19.5 | Caption should be "Computing Network Measures in R" |
| p. 528 | Available Data should read: "Part of the historic information is available in the file bicup2006.csv. The file contains the historic information with known demand for a 3-week period, separated into 15-minute intervals, and dates and times for a future 3-day period (DEMAND = NaN), for which forecasts should be generated (as part of the 2006 competition)." |
| p. 533, Data Files Used in the Book |
File Amtrak data.csv should be Amtrak.csv |
| END ERRATA |
