Download Empirical Evaluation (Ch 5)

Empirical Evaluation (Ch 5)
• how accurate is a hypothesis/model/dec.tree?
• given 2 hypotheses, which is better?
• accuracy on training set is biased
– error: errortrain(h) = #misclassifications/|Strain|
– errorD(h) ≥ errortrain(h)
• could set aside a random subset of data for testing
– sample error for any finite sample S drawn randomly from D is
unbiased, but not necessarily same as true error
– errS(h) ≠ errD(h)
• what we want is estimate of “true” accuracy over
distribution D
Confidence Intervals
• put a bound on errorD(h) based on Binomial
Distribution
– suppose sample error rate is errorS(h)=p
– then 95% CI for errorD(h) is
–
–
–
–
E[errorD(h)] = errorS(h) = p
E[var(errorD(h))] = p(1-p)/n
standard deviation = s = var; var = s2
1.96s comes from confidence level (95%)
Binomial Distribution
• put a bound on errorD(h) based on Binomial
distribution
– suppose true error rate is errorD(h)=p
– on a sample of size n, would expect np errors on
average, but could vary around that due to
sampling
(error rate, as a proportion:)
Hypothesis Testing
• is errorD(h)<0.2 (is error rate of h less than 20%?)
– example: is better than majority classifier? (suppose
errormaj(h)=20%)
• if we approximate Binomial as Normal, then
m±2s should bound 95% of likely range for
errorD(h)
• two-tailed test:
– risk of true error being higher or lower is 5%
– Pr[Type I error]≤0.05
• restrictions: n≥30 or np(1-p)≥5
• Gaussian Distribution
1.28s = 80% of distr.
z-score:
relative distance
of a value x from
mean
• for a one-tailed test, use z value for a/2
• for example
•
•
•
•
suppose errorS(h)=0.19 and s=0.02
suppose you want 95% confidence that errorD(h)<20%,
then test if 0.2-errorS(h)>1.64s
1.64 comes from z-score for a=90%
• notice that confidence interval on error rate tightens
with larger sample sizes
– example: compare 2 trials that have 10% error
– test set A has 100 examples, h makes 10 errors:
• 10/100 sqrt(.1x.9/100)=0.03
• CI95%(err(h)) = [10±6%] = [4-16%]
– test set B has 100,000 examples, 10,000 errors:
• 10,000/100,000=sqrt(.1x.9/100,000)=0.00095
• CI95%(err(h)) = [10±0.19%] = [9.8-10.2%]
• Comparing 2 hypotheses (decision trees)
– test whether 0 is in conf. interval of difference
– add variances
example...
Estimating the accuracy
of a Learning Algorithm
• errorS(h) is the error rate of a particular
hypothesis, which depends on training data
• what we want is estimate of error on any
training set drawn from the distribution
• we could repeat the splitting of data into
independent training/testing sets, build and
test k decisions trees, and take average
k-fold Cross-Validation
partition the dataset D into k subsets
of equal size (30), T1..Tk
for i from 1 to k do:
Si = D-Ti // training set, 90% of D
hi = L(Si) // build decision tree
ei = error(hi,Ti) // test d-tree on 10% held out
m = (1/k)Sei
s = (1/k)S(ei-m)2
SE = s/k  (1/k(k-1))S(ei-m)2
CI95 = m  tdof,a SE (tdof,a=2.23 for k=10 and a=95%)
k-fold Cross-Validation
• Typically k=10
• note that this is a biased estimator, probably under-estimates true accuracy
because uses less examples
• this is a disadvantage of CV: building d-trees with only 90% of the data
• (and it takes 10 times as long)
• what to do with 10 accuracies from CV?
– accuracy of alg is just the mean (1/k)Sacci
– for CI, use “standard error” (SE):
• s = (1/k)S(ei-m)2
• SE = s/k  (1/k(k-1))S(ei-m)2
• standard deviation for estimate of the mean
– 95% CI = m ± tdof,a (1/k(k-1))S(ei-m)2
• Central Limit Theorem
– we are estimating a “statistic” (parameter of a distribution, e.g. the mean)
from multiple trials
– regardless of underlying distribution, estimate of the mean approaches a
Normal distribution
– if std. dev. of underlying distribution is s, then std. dev. of mean of
distribution is s/n
• example: multiple trials of testing the accuracy of a learner,
assuming true acc=70% and s=7%
• there is intrinsic variability in accuracy between different trials
• with more trials, distribution converges to underlying (std. dev. stays around 7)
• but the estimate of the mean (vertical bars, m2s/n) gets tighter
est of true mean=
71.02.5
est of true mean=
70.50.6
est of true mean=
69.980.03
• Student’s T distribution is similar to Normal distr.,
but adjusted for small sample size; dof = k-1
• example: t9,0.05 = 2.23 (Table 5.6)
• Comparing 2 Learning Algorithms
– e.g. ID3 with 2 different pruning methods
• approach 1:
– run each algorithm 10 times (using CV)
independently to get CI for acc of each alg
• acc(A), SE(A)
• acc(B), SE(B)
– T-test: statistical test if difference means ≠ 0
• d=acc(A)-acc(B)
– problem: the variance is additive
(unpooled)
58
60
62
64
66
68
-3
acc(LB,Ti) d=B-A
59
+2
63
+1
63
+4
63
+3
64
+2
62
+3
63%
+3%, SE=1%
mean diff is same but B is
systematically higher than A
3
6
9
d=acc(B)-acc(A)
mean = 3%
SE =  3.7 (just a guess)
suppose mean acc for A is 61%2,
mean acc for B is 64%2
acc(LA,Ti)
58
62
59
60
62
59
60%
0
-3
0
3
6
9
• approach 2: Paired T-test
– run the algorithms in parallel on the same
divisions of tests
test whether 0 is in CI of differences: