title
Prediction study design
subtitle
author
Jeffrey Leek
job
Johns Hopkins Bloomberg School of Public Health
logo
bloomberg_shield.png
framework
io2012
highlighter
highlight.js
hitheme
tomorrow
url
lib
assets
../../librariesNew
../../assets
widgets
mode
selfcontained
Define your error rate
Split data into:
Training, Testing, Validation (optional)
On the training set pick features
On the training set pick prediction function
If no validation
If validation
Apply to test set and refine
Apply 1x to validation
http://www.heritagehealthprize.com/c/hhp/leaderboard
http://www2.research.att.com/~volinsky/papers/ASAStatComp.pdf
Used by the professionals
http://www.kaggle.com/
Suppose you are predicting a binary outcome
Diseased/healthy
Click on ad/not click on ad
One classifier is flipping a coin
Probability of perfect classification is approximately:
$\left(\frac{1}{2}\right)^{sample ; size}$
$n = 1$ flipping coin 50% chance of 100% accuracy
$n = 2$ flipping coin 25% chance of 100% accuracy
$n = 10$ flipping coin 0.10% chance of 100% accuracy
Rules of thumb for prediction study design
If you have a large sample size
60% training
20% test
20% validation
If you have a medium sample size
If you have a small sample size
Do cross validation
Report caveat of small sample size
Some principles to remember
Set the test/validation set aside and don't look at it
In general randomly sample training and test
Your data sets must reflect structure of the problem
If predictions evolve with time split train/test in time chunks (calledbacktesting in finance)
All subsets should reflect as much diversity as possible
Random assignment does this
You can also try to balance by features - but this is tricky