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一、 考虑表中二元分类问题得训练样本集
1. 整个训练样本集关于类属性得熵就是多少?
2. 关于这些训练集中a1,a2得信息增益就是多少?
3. 对于连续属性a3,计算所有可能得划分得信息增益.
4. 根据信息增益,a1,a2,a3哪个就是最佳划分?
5. 根据分类错误率,a1,a2哪具最佳?
6. 根据gini指标,a1,a2哪个最佳?
答1、
P(+) = 4/9 and P(−) = 5/9
−4/9 log2(4/9) − 5/9 log2(5/9) = 0、9911、
答2:
(估计不考)
答3:
答4: According to information gain, a1 produces the best split、
答5:
For attribute a1: error rate = 2/9、
For attribute a2: error rate = 4/9、
Therefore, according to error rate, a1 produces the best split、
答6:
二、 考虑如下二元分类问题得数据集
1. 计算a、b信息增益,决策树归纳算法会选用哪个属性
2. 计算a、b gini指标,决策树归纳会用哪个属性?
这个答案没问题
3. 从图4-13可以瞧出熵与gini指标在[0,0、5]都就是单调递增,而[0、5,1]之间单调递减。有没有可能信息增益与gini指标增益支持不同得属性?解释您得理由
Yes, even though these measures have similar range and monotonous
behavior, their respective gains, Δ, which are scaled differences of the
measures, do not necessarily behave in the same way, as illustrated by
the results in parts (a) and (b)、
贝叶斯分类
1. P(A = 1|−) = 2/5 = 0、4, P(B = 1|−) = 2/5 = 0、4,
P(C = 1|−) = 1, P(A = 0|−) = 3/5 = 0、6,
P(B = 0|−) = 3/5 = 0、6, P(C = 0|−) = 0; P(A = 1|+) = 3/5 = 0、6,
P(B = 1|+) = 1/5 = 0、2, P(C = 1|+) = 2/5 = 0、4,
P(A = 0|+) = 2/5 = 0、4, P(B = 0|+) = 4/5 = 0、8,
P(C = 0|+) = 3/5 = 0、6、
2.
3. P(A = 0|+) = (2 + 2)/(5 + 4) = 4/9,
P(A = 0|−) = (3+2)/(5 + 4) = 5/9,
P(B = 1|+) = (1 + 2)/(5 + 4) = 3/9,
P(B = 1|−) = (2+2)/(5 + 4) = 4/9,
P(C = 0|+) = (3 + 2)/(5 + 4) = 5/9,
P(C = 0|−) = (0+2)/(5 + 4) = 2/9、
4. Let P(A = 0,B = 1, C = 0) = K
5. 当得条件概率之一就是零,则估计为使用m-估计概率得方法得条件概率就是更好得,因为我们不希望整个表达式变为零。
1. P(A = 1|+) = 0、6, P(B = 1|+) = 0、4, P(C = 1|+) = 0、8, P(A =
1|−) = 0、4, P(B = 1|−) = 0、4, and P(C = 1|−) = 0、2
2、
Let R : (A = 1,B = 1, C = 1) be the test record、 To determine its
class, we need to pute P(+|R) and P(−|R)、 Using Bayes theorem, P(+|R) = P(R|+)P(+)/P(R) and P(−|R) = P(R|−)P(−)/P(R)、
Since P(+) = P(−) = 0、5 and P(R) is constant, R can be classified by
paring P(+|R) and P(−|R)、
For this question,
P(R|+) = P(A = 1|+) × P(B = 1|+) × P(C = 1|+) = 0、192
P(R|−) = P(A = 1|−) × P(B = 1|−) × P(C = 1|−) = 0、032
Since P(R|+) is larger, the record is assigned to (+) class、
3、
P(A = 1) = 0、5, P(B = 1) = 0、4 and P(A = 1,B = 1) = P(A) ×
P(B) = 0、2、 Therefore, A and B are independent、
4、
P(A = 1) = 0、5, P(B = 0) = 0、6, and P(A = 1,B = 0) = P(A =1)× P(B = 0) = 0、3、 A and B are still independent、
5、
pare P(A = 1,B = 1|+) = 0、2 against P(A = 1|+) = 0、6 and
P(B = 1|Class = +) = 0、4、 Since the product between P(A = 1|+)
and P(A = 1|−) are not the same as P(A = 1,B = 1|+), A and B are
not conditionally independent given the class、
三、 使用下表中得相似度矩阵进行单链与全链层次聚类。绘制树状况显示结果,树状图应该清楚地显示合并得次序。
There are no apparent relationships between s1, s2, c1, and c2、
ﻬ
A2: Percentage of frequent itemsets = 16/32 = 50、0% (including the null
set)、
A4:ﻩFalse alarm rate is the ratio of I to the total number of itemsets、 Since
the count of I = 5, therefore the false alarm rate is 5/32 = 15、6%、
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