Preface
1 Introduction to Machine Learning Part Three
2 Evolutionary Operations
3 Multiple Treatments
4 Multiple Endpoints
5 Optimal Binning
6 Exact P-Values
7 Probit Regression
8 Over-dispersion
9 Random Effects
10 Weighted Least Squares
11 Multiple Response Sets
12 Complex Samples
13 Runs Tests
14 Decision Trees15 Spectral Plots
16 Newton's Methods
17 Stochastic Processes, Stationary Markov Chains
18 Stochastic Processes, Absorbing Markov Chains
19 Conjoint Models
20 Machine Learning and Unsolved Questions
Index
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