Statistics: Difference between revisions
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{| border="1" style="border-collapse:collapse; text-align:center;" | {| border="1" style="border-collapse:collapse; text-align:center;" | ||
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| | | Predict | Predict | | |||
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| True | 1 | TP | FN | Sens=TP/(TP+FN) | | True | 1 | TP | FN | Sens=TP/(TP+FN) | ||
Revision as of 12:59, 1 April 2013
Boxcox transformation
Finding transformation for normal distribution
Visualize the random effects
http://www.quantumforest.com/2012/11/more-sense-of-random-effects/
Sensitivity/Specificity/Accuracy
| | Predict | Predict | |
| 1 | TP | FN | Sens=TP/(TP+FN) |
| 0 | FP | TN | Spec=TN/(FP+TN) |
| | | | N = TP + FP + FN + TN |
- Sensitivity = TP / (TP + FN)
- Specificity = TN / (TN + FP)
- Accuracy = (TP + TN) / N
ROC curve and Brier score
Elements of Statistical Learning
Bagging
Chapter 8 of the book.
- Bootstrap mean is approximately a posterior average.
- Bootstrap aggregation or bagging average: Average the prediction over a collection of bootstrap samples, thereby reducing its variance. The bagging estimate is defined by
- [math]\displaystyle{ \hat{f}_{bag}(x) = \frac{1}{B}\sum_{b=1}^B \hat{f}^{*b}(x). }[/math]
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