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;" | ||
|- | |- | ||
! | ! || || Predict || Predict || | ||
|- | |- | ||
| True || 1 || TP || FN || Sens=TP/(TP+FN) | | True || 1 || TP || FN || Sens=TP/(TP+FN) | ||
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* Sensitivity = TP / (TP + FN) | * Sensitivity = TP / (TP + FN) | ||
* Specificity = TN / (TN + FP) | * Specificity = TN / (TN + FP) | ||
* Accuracy = (TP + TN) / | * Accuracy = (TP + TN) / N | ||
== ROC curve and Brier score == | == ROC curve and Brier score == | ||
Revision as of 12:57, 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 | |||
|---|---|---|---|---|
| True | 1 | TP | FN | Sens=TP/(TP+FN) |
| True | 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]
- ksjlfda