List of Tables

Table 1.1 The Contact Lens Data 7
Table 1.2 The Weather Data 11
Table 1.3 Weather Data With Some Numeric Attributes 12
Table 1.4 The Iris Data 15
Table 1.5 The CPU Performance Data 16
Table 1.6 The Labor Negotiations Data 17
Table 1.7 The Soybean Data 20
Table 2.1 Iris Data as a Clustering Problem 46
Table 2.2 Weather Data With a Numeric Class 47
Table 2.3 Family Tree 48
Table 2.4 The Sister-of Relation 49
Table 2.5 Another Relation 52
Table 3.1 A New Iris Flower 80
Table 3.2 Training Data for the Shapes Problem 83
Table 4.1 Evaluating the Attributes in the Weather Data 94
Table 4.2 The Weather Data, With Counts and Probabilities 97
Table 4.3 A New Day 98
Table 4.4 The Numeric Weather Data With Summary Statistics 101
Table 4.5 Another New Day 102
Table 4.6 The Weather Data with Identification Codes 111
Table 4.7 Gain Ratio Calculations for the Tree Stumps of Fig. 4.2 112
Table 4.8 Part of the Contact Lens Data for which Astigmatism=Yes 116
Table 4.9 Part of the Contact Lens Data for Which Astigmatism=Yes and Tear Production Rate=Normal 117
Table 4.10 Item Sets for the Weather Data With Coverage 2 or Greater 121
Table 4.11 Association Rules for the Weather Data 123
Table 5.1 Confidence Limits for the Normal Distribution 166
Table 5.2 Confidence Limits for Student’s Distribution With 9 Degrees of Freedom 174
Table 5.3 Different Outcomes of a Two-Class Prediction 180
Table 5.4 Different Outcomes of a Three-Class Prediction: (A) Actual; (B) Expected 181
Table 5.5 Default Cost Matrixes: (A) Two-Class Case; (B) Three-Class Case 182
Table 5.6 Data for a Lift Chart 184
Table 5.7 Different Measures Used to Evaluate the False Positive Versus False Negative Tradeoff 191
Table 5.8 Performance Measures for Numeric Prediction 195
Table 5.9 Performance Measures for Four Numeric Prediction Models 197
Table 6.1 Preparing the Weather Data for Insertion Into an FP-tree: (A) The Original Data; (B) Frequency Ordering of Items With Frequent Item Sets in Bold; (C) The Data With Each Instance Sorted Into Frequency Order; (D) The Two Multiple-Item Frequent Item Sets 236
Table 7.1 Linear Models in the Model Tree 280
Table 8.1 The First Five Instances From the CPU Performance Data; (A) Original Values; (B) The First Partial Least Squares Direction; (C) Residuals From the First Direction 308
Table 8.2 Transforming a Multiclass Problem Into a Two-Class One: (A) Standard Method; (B) Error-Correcting Code 324
Table 8.3 A Nested Dichotomy in the Form of a Code Matrix 327
Table 9.1 Highest Probability Words and User Tags From a Sample of Topics Extracted From a Collection of Scientific Articles 381
Table 9.2 Link Functions, Mean Functions, and Distributions Used in Generalized Linear Models 401
Table 10.1 Summary of Performance on the MNIST Evaluation 421
Table 10.2 Loss Functions, Corresponding Distributions, and Activation Functions 423
Table 10.3 Activation Functions and Their Derivatives 425
Table 10.4 Convolutional Neural Network Performance on the ImageNet Challenge 439
Table 10.5 Components of a “Long Short-Term Memory” Recurrent Neural Network 459
Table 13.1 The Top 10 Algorithms in Data Mining, According to a 2006 Poll 504
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