when calculating
$U_{CF}$(e,N)
CF: Confidence Level(here is 25%)
e:misclassifying counts of current subtree we focus on
N:counts of sub-datasets relevant to current subtree who is under judgment whether to be pruned or not.

“Pr” is short for “Pessimistic error rate”.

$U_{CF}$(e,N)=Pr=$\frac{e+0.5+\frac{Coef{f}^2}{2}+\sqrt{Coef{f}^2?\{(e+0.5）[1?\frac{e+0.5}{N}]+\frac{Coef{f}^2}{4}\}}}{N+Coef{f}^2}①$

The method to get $Coeff$ is as follows:

$\frac{Coeff?Deviation[i?1]}{Deviation[i]?Deviation[i?1]}=\frac{ConfidenceLevel?val[i?1]}{val[i]?val[i?1]}②$

Now Let’s analyse the meaning of ② and how to get “i” in formula ②.

What is Deviation and Val?
Val[] = { 0, 0.001, 0.005, 0.01, 0.05, 0.10, 0.20, 0.40, 1.00},
Deviation[] = {4.0, 3.09, 2.58, 2.33, 1.65, 1.28, 0.84, 0.25, 0.00};

for Normal Distribution：
P{X>Deviation[i]}=val[i],
for example:
P{X>2.58}=0.005
--------------------

Now let’s analyse the whole meaning of formula ②

the red line is $P(X>x)={\int }_x^{+\infty }\frac{1}{\sqrt{2\pi }}{e}^{?\frac{x^2}{2}}dx$

the blue line is $f(x)=\frac{1}{\sqrt{2\pi }}{e}^{?\frac{x^2}{2}}$
and the two points are:
(deviation[i],val[i])=(0.25,0.4)
(deviation[i-1,val[i-1])=(0.84,0.2)

Let’s amplify the above figure to the following picture:

so tan C=$\frac{Val[i]?Val[i?1]}{Deviation[i?1]?Deviation[i]}=\frac{ConfidenceLevel?Val[i?1]}{Deviation[i?1]?Coeff}$

then we can get ②and compute $Coeff$ and $Coef{f}^2$

but how to get “i” in above formula?
when $Val[i?1]＜Confidencelevel\le Val[i]$
is satisfied,
we get “i”.
when Confidence Level=0.25
i=7
val[i-1]=0.2
val[i]=0.4
deviation[i-1]=0.84
deviation[i]=0.25
substitute the above values in ②
we get Coeff=0.6925
then,
$Coef{f}^2=0.479556$
--------------------

**Now Let’s calculate **
$U_{0.25}(1,16)$
CF=0.25
e=1
N=16
$Coef{f}^2=0.479556$
We’ll get :
Pr=

$\frac{1+0.5+\frac{0.479556}{2}+\sqrt{0.479556?\{(1+0.5）[1?\frac{1+0.5}{16}]+\frac{0.479556}{4}\}}}{16+0.479556}$=0.156681

N·Pr=16×0.156681=2.507
Compared with Quinlan’s book page 42th:

Now Let’s calculate
$U_{0.25}(1,168)$
CF=0.25
e=1
N=168

Pr=$\frac{1+0.5+\frac{0.479556}{2}+\sqrt{0.479556?\{(1+0.5）[1?\frac{1+0.5}{168}]+\frac{0.479556}{4}\}}}{168+0.479556}=0.015536$

$N?U_{0.25}(1,168)=N?Pr=168?0.015536=2.61$
Compared with Quinlan’s Book on Page 42th:

The code to veriy the above value:

/*************************************************************************/ /* */ /* Statistical routines for C4.5 */ /* ----------------------------- */ /* */ /*************************************************************************/ #include <stdio.h> #include <math.h>/*************************************************************************/ /* */ /* Compute the additional errors if the error rate increases to the */ /* upper limit of the confidence level. The coefficient is the */ /* square of the number of standard deviations corresponding to the */ /* selected confidence level. (Taken from Documenta Geigy Scientific */ /* Tables (Sixth Edition), p185 (with modifications).) */ /* */ /*************************************************************************/ float CF=0.25;float Val[] = { 0, 0.001, 0.005, 0.01, 0.05, 0.10, 0.20, 0.40, 1.00},//概率Dev[] = {4.0, 3.09, 2.58, 2.33, 1.65, 1.28, 0.84, 0.25, 0.00};//這個就是分位點// P{X>Dev[i]}=val[i],// according to standardized normal distribution N(0,1)// for example:// P{X>2.58}=0.005,float AddErrs(float N, float e)//葉子的悲觀錯誤 {static float Coeff=0;float Val0, Pr;if ( ! Coeff )//compute only once when pruning trees{/* Compute and retain the coefficient value, interpolating fromthe values in Val and Dev */int i;i = 0;while ( CF > Val[i] ) i++;//這里可以看到，是根據(jù)置信度對悲觀錯誤數(shù)量進行計算的。//但是呢，也沒有像理論中直接計算“悲觀錯誤率”Coeff = Dev[i-1] +(Dev[i] - Dev[i-1]) * (CF - Val[i-1]) /(Val[i] - Val[i-1]);Coeff = Coeff * Coeff;printf("Coeff=%f\n",Coeff);//CF定下來以后，Coeff就是定的}if ( e < 1E-6 ){return N * (1 - exp(log(CF) / N));}elseif ( e < 0.9999 ){Val0 = N * (1 - exp(log(CF) / N));return Val0 + e * (AddErrs(N, 1.0) - Val0);//這里在進行遞歸調(diào)用}elseif ( e + 0.5 >= N ){return 0.67 * (N - e);}else{float fenzi;fenzi=(e + 0.5 + Coeff/2+ sqrt( Coeff * ((e + 0.5) * (1 - (e + 0.5)/N) + Coeff/4)) );printf("fenzi=%f\n",fenzi);Pr = (e + 0.5 + Coeff/2+ sqrt( Coeff * ((e + 0.5) * (1 - (e + 0.5)/N) + Coeff/4)) )/ (N + Coeff);printf("Pr=%f\n",Pr);return (N * Pr - e);} }int main() {float result=AddErrs(16, 1); printf("result=%f\n",result);exit(0);}

running method:

gcc -o stats stats.c -lm
./stats

the result is:
Coeff=0.479556
fenzi=2.582031
Pr=0.156681
result=1.506894

總結(jié)

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