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 |