#### Transcript Slide 1

```Model assessment and cross-validation
- overview
 Analytical validation methods (AIC, BIC)
 Resampling methods (cross-validation, bootstrap methods)
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Bias, variance and model complexity
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Expected prediction error
INSERT formula 7.8
We would like to minimize Err!
Increasing the complexity of models increases variance and decreases bias
Example: Smoothing based on nearest neighbours.
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Loss function, training error, and test error
- quantitative response (regression)
• Loss functions (examples)
• Training error:
• Test error:
where the expectation is taken over the joint distribution of
(X, Y)
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Loss function, training error, and test error
- qualitative response (classification)
Model: G=[1, 2, …, K], pk(X)=Pr(G=k| X).
Decision rule:
Gˆ  X   arg max k pˆ k  X 
Examples of loss functions:
• 0-1 Loss
INSERT form. 7.4
• Cross-entropy loss (log-likelihood)
INSERT form. 7.5
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Training error, and test error
- qualitative response (classification)
• Training error
– 0-1-loss: misclassification rate
– Cross-entropy loss:
2
err  
N
N
 log pˆ
i 1
Gi
( xi )
• Test error


Err  E L G, Gˆ  X 
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Model selection
Assume that the given model fα(x) is dependent on some tuning
parameter (model complexity parameter) α
Examples of α:
• No. predictors (multiple regression)
• Degree of a polynomial (polynomial regression)
• Penalty factor (smoothing splines, ridge regression)
• Window width (kernels)
The aim of model selection: To find a model having minimum
test error
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Model selection and assessment
Model selection
Estimate the performance of different models in order to
choose the best one.
Model assessment
Having chosen the final model, estimate the test error
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Model selection and assessment
in data-rich applications
•
Training set (to produce a fit, appr. 50%)
•
Validation set (for model selection, 25 %)
•
Test set (for model assessment, 25%)
Example (splines)
1. Fit the training set using models with smoothing factors λ1, …, λn
2. Using fitted splines f1*(x),…fn*(x), estimate the prediction error using the
validation set and choose the model #i producing minimal error.
3. Estimate the generalization error using the test set and model #i
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Model selection and assessment in applications
with insufficient data
• Analytical expressions to select and assess models
– Cp (correction for the number of inputs or basis functions)
– AIC (Akaike’s information criterion)
– BIC (Bayesian information criterion)
• Resampling
– Cross-validation
– The bootstrap
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Analytical validation methods
Background:
A model typically overfits the data.
The prediction error will on average be higher than the
training error
Terminology:
The difference between the average training and prediction
error is called optimism
Basic idea:
Find an analytical expression for the optimism.
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Optimism
• Training error rate:
• In-sample error:
1
Errin  EY 
N
N
 L(Y
i 1
i
new

ˆ
, f ( xi )) 

• Optimism:
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Analytical expressions for the optimism
1. For squared error, 0-1 loss, and some other loss functions
2. For linear models with d inputs or basis functions
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Cp scores
Basic idea:
d 2
Errin  EY (err )  2  
N
when d parameters are fitted under squared loss.
Compute
and choose the model with smallest Cp score
Properties:
•
Penalization inreases with increasing model complexity (d)
and decreases as the training sample size increases
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Akaike’s information criterion
When the likelihood is maximized it holds asymptotically that
where
Given a tuning parameter , we set
where d() is the effective number of parameters.
For Gaussian models, AIC is equivalent to Cp
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Effective number of parameters (d())
For linear smoothers:
yˆ  Sy
Examples:
• Simple linear regression (#exact param), ridge regression
• Smoothing splines
• Kernel smoothers
Define the effective number of parameters as
d S  traceS
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Bayesian information criterion
Based on Bayesian theory we set
• For Gaussian models
Properties:
• BIC =AIC if ”2” is substituted for log(N)
• Since log(N)>1 for N>7, BIC penalizes complex models more
heavily than AIC
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Features of AIC and BIC
For large models (assymptotical property)
• BIC chooses the right model (if it is present among
alternatives)
• AIC chooses too complex models
For small models
• BIC chooses too simple models
• AIC is OK
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Resampling methods
Cross-validation
K-fold cross-validation (rough scheme, show picture):
1. Divide data-set in K roughly equally-sized subsets
2. Remove subset #i and fit the model using remaining data.
3. Predict the function values for subset #i using the fitted
model.
4. Repeat steps 2-3 for different i
5. CV= squared difference between observed values and
predicted values (another function is possible)
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Resampling methods
Cross-validation
Note: if K=N then method is leave-one-out cross-validation.
K-fold cross-validation:
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Model selection using cross-validation
Having a model depending on a tuning (complexity)
parameter, choose the one with smallest CV:
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Prediction error and cross-validation curve
estimated from a single training set
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Generalized cross-validation
Basic idea: approximate the leave-one-out CV to make it faster
Used for linear smoothers:
yˆ  Sy
Note: In smoothing problems, GCV is similiar to AIC
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The bootstrap
Why do we need the bootstrap?
• To estimate the uncertainty of parameter estimates
Example:
• Having a sample X1, …, Xn from an unknown distribution
we estimate its mean (expectation) by computing the sample mean
• How to find uncertainty of the sample mean?
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Implementation of the crude nonparametric
bootstrap
1. Sample with replacement form the observed data to obtain
a bootstrap sample
2. Repeat step 1 B times
3. Compute parameter estimates using the bootstrap samples
4. Compute the variance of estimates from step 3
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Bootstrap replicates
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Benefits and drawbacks of the nonparametric
bootstrap
1. The uncertainty of an estimate can be obtained without any
2. It is not applicable to small data sets
3. If the distribution is known (except for the level of some
parameters) the bootstarp may be slightly less efficient than
conventional parametric methods
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The bootstrap and estimation of prediction errors
• Fit the model to bootstrap samples (role=training) and
examine how well it predicts the training set (role=prediction)
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An improved bootstrap estimator of the
prediction error
The leave-one-out bootstrap is similar to two-fold CV, and
produces biased estimates of the expected squared
prediction error.
Solution:
.632-estimator(pulls bias down)
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