1. Where does the error come from?
Disclaimer:
This PPT is modified based on
Dr. Hung-yi Lee

2. Bias and variability: Arrow shooting as an example
Figure 3.14
Introduction to the Practice of Statistics, Sixth Edition
© 2009 W.H. Freeman and Company

3. Review Average Error on Testing Data
error due to "bias" and error due to "variance"
We have to know where is the error come from?
Model Complexity
A more complex model does not always lead to better performance on testing data.

4. Estimator 𝑦 = 𝑓 𝑓 Bias + Variance 𝑓 ∗ Only Niantic knows 𝑓
f_hat: ground truth
f^*: estimation
From training data, we find 𝑓 ∗ 𝑓
𝑓 ∗ is an estimator of 𝑓

5. Bias and Variance of Estimator
unbiased
Estimate the mean of a variable x
assume the mean of x is 𝜇
assume the variance of x is 𝜎 2
Estimator of mean 𝜇
Sample N points: 𝑥 1 , 𝑥 2 ,…, 𝑥 𝑁
𝑚 2
𝑚 5
𝑚 1 𝜇
𝑚= 1 𝑁 𝑛 𝑥 𝑛 ≠𝜇
Red point: ground truth
𝑚 4
𝑚 3
𝐸 𝑚 =𝐸 1 𝑁 𝑛 𝑥 𝑛
= 1 𝑁 𝑛 𝐸 𝑥 𝑛 =𝜇
𝑚 6

6. Bias and Variance of Estimator
unbiased
Estimate the mean of a variable x
assume the mean of x is 𝜇
assume the variance of x is 𝜎 2
Estimator of mean 𝜇
Sample N points: 𝑥 1 , 𝑥 2 ,…, 𝑥 𝑁
Smaller N
Larger N
𝑚= 1 𝑁 𝑛 𝑥 𝑛 ≠𝜇
Var(x_bar) = sigma/N 𝜇 𝜇
Variance depends on the number of samples
V𝑎𝑟 𝑚 = 𝜎 2 𝑁

7. Bias and Variance of Estimator
Increase N
Estimate the mean of a variable x
assume the mean of x is 𝜇
assume the variance of x is 𝜎 2
Estimator of variance 𝜎 2
Sample N points: 𝑥 1 , 𝑥 2 ,…, 𝑥 𝑁
𝑠 3
𝜎 2
𝜎 2
𝑠 1
𝑚= 1 𝑁 𝑛 𝑥 𝑛
𝑠= 1 𝑁 𝑛 𝑥 𝑛 −𝑚 2
𝑠 4
𝑠 5
Biased estimator
𝑠 2
𝑠 6
𝐸 𝑠 = 𝑁−1 𝑁 𝜎 2
≠ 𝜎 2

8. 𝐸 𝑓 ∗ = 𝑓 𝑓 ∗ 𝑓 Variance Bias
𝐸 𝑓 ∗ = 𝑓
Variance
𝑓 ∗
If we can do the experiments several times
f_hat: truth
f^*: sample mean
Bias 𝑓

9. Parallel Universes
In all the universes, we are collecting (catching) 10 Pokémons as training data to find 𝑓 ∗
Universe 1
Universe 2
Universe 3

10. Parallel Universes
In different universes, we use the same model, but obtain different 𝑓 ∗
Universe 123
Universe 345
Same model. With different inputted data, the regression lines are different
y = b + w ∙ xcp
y = b + w ∙ xcp

11. 𝑓 ∗ in 100 Universes y = b + w ∙ xcp y = b + w1 ∙ xcp + w2 ∙ (xcp)2
100 re-samples;
With three different polynomial regression models
y = b + w1 ∙ xcp + w2 ∙ (xcp)2
+ w3 ∙ (xcp)3 + w4 ∙ (xcp)4
+ w5 ∙ (xcp)5

12. Simpler model is less influenced by the sampled data
Variance
y = b + w1 ∙ xcp + w2 ∙ (xcp)2
+ w3 ∙ (xcp)3 + w4 ∙ (xcp)4
+ w5 ∙ (xcp)5
y = b + w ∙ xcp
Left: larger bias, smaller variance
Right: smaller bias, larger variance
f(x) = 5: variance equal to zero
Small
Variance
Large
Variance
Simpler model is less influenced by the sampled data
Consider the extreme case f(x) = 5

13. Bias 𝐸 𝑓 ∗ = 𝑓 Bias: If we average all the 𝑓 ∗ , is it close to 𝑓 ?
𝐸 𝑓 ∗ = 𝑓
Bias: If we average all the 𝑓 ∗ , is it close to 𝑓 ?
Large
Bias
Assume this is 𝑓
We don’t really know the F^
Small
Bias

14. Black curve: the true function 𝑓 Red curves: 5000 𝑓 ∗
Degree=1
Black curve: the true function 𝑓
Red curves: 𝑓 ∗
Blue curve: the average of 𝑓 ∗
= 𝑓
Degree=5
Degree=3

15. Bias Large Small Bias Bias y = b + w1 ∙ xcp + w2 ∙ (xcp)2
Function space is getting bigger when model gets more complicated
Large
Bias
Small
Bias
model
model

16. Bias v.s. Variance Underfitting Overfitting Large Bias Small Bias
Error from bias
Error from variance
Error observed
Underfitting
Overfitting
We have to know where is the error come from?
Simple model  complex model
Large Bias
Small Bias
Small Variance
Large Variance

17. What to do with large bias?
Diagnosis:
If your model cannot even fit the training examples, then you have large bias
If you can fit the training data, but large error on testing data, then you probably have large variance
For bias, redesign your model:
Add more features as input
A more complex model
Underfitting
Overfitting
large bias

18. What to do with large variance?
Collect more data
Regularization
Very effective, but not always practical
10 examples
100 examples
May increase bias
How can I know
simulation to have more data.
Second column: left (no regularization)  mid (some regularization)  right (More regularization).
no regularization
Some regularization
More regularization

19. Model Selection Training Set Testing Set Real Testing Set
There is usually a trade-off between bias and variance.
Select a model that balances two kinds of error to minimize total error
What you should NOT do:
Training Set
Testing Set
Real Testing Set
Model 1
Err = 0.9
(not in hand)
Model 2
Err = 0.7
Model 3
Err = 0.5
Err > 0.5

20. Kaggle competition Training Set Testing Set Testing Set public private
Model 1
Err = 0.9
Model 2
Err = 0.7
Model 3
Err = 0.5
Err > 0.5
I beat baseline!
No, you don’t
What will happen?
Public testing set is available for baseline;
Private testing set is only available after submission deadline in Kaggle.

21. Cross Validation Training Set Testing Set Testing Set Training Set
public
private
Training Set
Testing Set
Testing Set
Training Set
Validation
set
Using the results of public testing data to tune your model
You are making public set better than private set.
For validation set method
Model 1
Err = 0.9
Model 2
Err = 0.7
Not recommend
Model 3
Err = 0.5
Err > 0.5
Err > 0.5

22. N-fold Cross Validation (eg: N=3)
Training Set
Model 1
Model 2
Model 3
Train
Train
Val
Err = 0.2
Err = 0.4
Err = 0.4
Train
Val
Train
Err = 0.4
Err = 0.5
Err = 0.5
Val
Train
Train
Err = 0.3
Err = 0.6
Err = 0.3
Model selection by N-fold CV.
Avg Err
= 0.3
Avg Err
= 0.5
Avg Err
= 0.4
Testing Set
Testing Set
public
private

23. Reference
Bishop: Chapter 3.2