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Statistical learning for biomedical data

Author: James D Malley; Karen G Malley; Sinisa Pajevic
Publisher: Cambridge ; New York : Cambridge University Press, 2011.
Series: Practical guides to biostatistics and epidemiology.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Malley, James D.
Statistical learning for biomedical data.
Cambridge : Cambridge University Press, 2011
(DLC) 2011377705
(OCoLC)663441381
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: James D Malley; Karen G Malley; Sinisa Pajevic
ISBN: 9780511993121 0511993129 9780511989308 051198930X 9780511975820 0511975821
OCLC Number: 704992965
Description: 1 online resource (xii, 285 pages) : illustrations.
Contents: pt. 1. Introduction --
pt. 2. A machine toolkit --
pt. 3. Analysis fundamentals --
pt. 4. Machine strategies. Part I. Introduction --
1. Prologue --
1.1. Machines that learn --
some recent history --
1.2. Twenty canonical questions --
1.3. Outline of the book --
1.4. A comment about example datasets --
1.5. Software --
2. The landscape of learning machines --
2.1. Introduction --
2.2. Types of data for learning machines --
2.3. Will that be supervised or unsupervised? --
2.4. An unsupervised example --
2.5. More lack of supervision --
where are the parents? --
2.6. Engines, complex and primitive --
2.7. Model richness means what, exactly? --
2.8. Membership or probability of membership? --
2.9. A taxonomy of machines? --
2.10. A note of caution --
one of many --
2.11. Highlights from the theory --
3. A mangle of machines --
3.1. Introduction --
3.2. Linear regression --
3.3. Logistic regression --
3.4. Linear discriminant --
3.5. Bayes classifiers --
regular and naïve --
3.6. Logic regression --
3.7. k-Nearest neighbors --
3.8. Support vector machines --
3.9. Neural networks --
3.10. Boosting --
3.11. Evolutionary and genetic algorithms --
4. Three examples and several machines --
4.1. Introduction --
4.2. Simulated cholesterol data --
4.3. Lupus data --
4.4. Stroke data --
4.5. Biomedical means unbalanced --
4.6. Measures of machine performance --
4.7. Linear analysis of cholesterol data --
4.8. Nonlinear analysis of cholesterol data --
4.9. Analysis of the lupus data --
4.10. Analysis of the stroke data --
4.11. Further analysis of the lupus and stroke data --
Part II. A machine toolkit --
5. Logistic regression --
5.1. Introduction --
5.2. Inside and around the model --
5.3. Interpreting the coefficients --
5.4. Using logistic regression as a decision rule --
5.5. Logistic regression applied to the cholesterol data --
5.6. A cautionary note --
5.7. Another cautionary note --
5.8. Probability estimates and decision rules --
5.9. Evaluating the goodness-of-fit of a logistic regression model --
5.10. Calibrating a logistic regression --
5.11. Beyond calibration --
5.12. Logistic regression and reference models --
6. A single decision tree --
6.1. Introduction --
6.2. Dropping down trees --
6.3. Growing a tree --
6.4. Selecting features, making splits --
6.5. Good split, bad split --
6.6. Finding good features for making splits --
6.7. Misreading trees --
6.8. Stopping and pruning rules --
6.9. Using functions of the features --
6.10. Unstable trees? --
6.11. Variable importance --
growing on trees? --
6.12. Permuting for importance --
6.13. The continuing mystery of trees --
7. Random Forests --
trees everywhere --
7.1. Random Forests in less than five minutes --
7.2. Random treks through the data --
7.3. Random treks through the features --
7.4. Walking through the forest --
7.5. Weighted and unweighted voting --
7.6. Finding subsets in the data using proximities --
7.7. Applying Random Forests to the Stroke data --
7.8. Random Forests in the universe of machines --
Part III. Analysis fundamentals --
8. Merely two variables --
8.1. Introduction --
8.2. Understanding correlations --
8.3. Hazards of correlations --
8.4. Correlations big and small --
9. More than two variables --
9.1. Introduction --
9.2. Tiny problems, large consequences --
9.3. Mathematics to the rescue? --
9.4. Good models need not be unique --
9.5. Contexts and coefficients --
9.6. Interpreting and testing coefficients in models --
9.7. Merging models, pooling lists, ranking features --
10. Resampling methods --
10.1. Introduction --
10.2. The bootstrap --
10.3. When the bootstrap works --
10.4. When the bootstrap doesn't work --
10.5. Resampling from a single group in different ways --
10.6. Resampling from groups with unequal sizes --
10.7. Resampling from small datasets --
10.8. Permutation methods --
10.9. Still more on permutation methods --
11. Error analysis and model validation --
11.1. Introduction --
11.2. Errors? What errors? --
11.3. Unbalanced data, unbalanced errors --
11.4. Error analysis for a single machine --
11.5. Cross-validation error estimation --
11.6. Cross-validation or cross-training? --
11.7. The leave-one-out method --
11.8. The out-of-bag method --
11.9. Intervals for error estimates for a single machine --
11.10. Tossing random coins into the abyss --
11.11. Error estimates for unbalanced data --
11.12. Confidence intervals for comparing error values --
11.13. Other measures of machine accuracy --
11.14. Benchmarking and winning the lottery --
11.15. Error analysis for predicting continuous outcomes --
Part IV. Machine strategies --
12. Ensemble methods --
let's take a vote --
12.1. Pools of machines --
12.2. Weak correlation with outcome can be good enough --
12.3. Model averaging --
13. Summary and conclusions --
13.1. Where have we been? --
13.2. So many machines --
13.3. Binary decision or probability estimate? --
13.4. Survival machines? Risk machines? --
13.5. And where are we going?
Series Title: Practical guides to biostatistics and epidemiology.
Responsibility: James D. Malley, Karen G. Malley, Sinisa Pajevic.

Abstract:

This highly motivating introduction to statistical learning machines explains underlying principles in nontechnical language, using many examples and figures.  Read more...

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'The book is well written and provides nice graphics and numerous applications.' Michael R. Chernick, Technometrics

 
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