markdown表格问题

name of post-pruning algorithm	name of article or book	year	inventer	the tree pruned	Remark
Pessimistic Pruning	《Simplifying Decision Trees》part2.3	1986b(也有1987b的说法，这里以论文上写的时间为准)	Quinlan	C4.5	Ross Quinlan　invented “Pessimistic Pruning”，John Mingers rename it as “Pessimistic Error Pruning” in his article《An Empirical Comparison of Pruning Methods for Decision Tree induction》

Column 1	Column 2
centered 文本居中	right-aligned 文本居右

name of post-pruning algorithm	name of article or book	year	inventer	the tree pruned	Remark

name of post-pruning algorithm	name of article or book	year	inventer	the tree pruned	Remark

name of post-pruning algorithm	name of article or book	year	inventer	the tree pruned	Remark
Pessimistic Pruning	《Simplifying Decision Trees》part2.3	1986b(也有1987b的说法，这里以论文上写的时间为准)	Quinlan	C4.5	Ross Quinlan　invented “Pessimistic Pruning”，John Mingers rename it as “Pessimistic Error Pruning” in his article《An Empirical Comparison of Pruning Methods for Decision Tree induction》
Reduced Error Pruning	《Simplifying Decision Trees》part2.2	1986b	Quinlan	C4.5	需要额外的验证集才能剪枝
Cost-Complexity Pruning	《Classification and Regression Trees》3.3节	1984	L Breiman	CART	针对分类树剪枝
Error-Complexity Pruning	《Classification and Regression Trees》8.5.1节	1984	L Breiman	CART	针对回归树剪枝，ＥＣＰ是在ＣＣＰ的基础上发展而来
Critical Value Pruning	《Expert System-Rule Induction with Statistical Data》,还有一说是:《An Empirical Comparison of Pruning Methods for Decision Tree Induction》但是该文作者自己说是引用自1987年的论文	1987a	John Mingers	论文中没有明说哪一种	关于CVP算法的出处众说纷纭，这里的出处是以《An Empirical Comparison of Pruning Methods for Ensemble Classifiers》P212提到的为准
Minimum-Error Pruning	《Learning decision rules in noisy domains》	1986	Niblett and Bratko	－	Can Not be Downloaded from Internet
Error-Based Pruning	《C4.5: Programs for Machine Learning 》4.2节	1993	Quinlan	C4.5	EBP is an evolution of PEP

分类树剪枝目的：
１．牺牲预测精度在可以接受的情况下，简化决策树（以便于提取知识）；
２．提高验证集精度（REP）
回归树剪枝目的：减缓、消除过拟合

剪枝代码汇总：
------------REP-finished--------
REP原理与具体实例：
https://blog.csdn.net/appleyuchi/article/details/83041047
REP剪枝代码实现：
https://github.com/appleyuchi/Decision_Tree_Prune/tree/master/ID3-REP-post_prune-Python-draw
----------------PEP-finished-----------------
PEP剪枝算法发展历史、原理和举例：
https://blog.csdn.net/appleyuchi/article/details/83795521
https://blog.csdn.net/appleyuchi/article/details/83902998
PEP-python-implemention：
https://blog.csdn.net/appleyuchi/article/details/83961060
-------------EBP-finished---------------------
EBP剪枝完整算法原理、C语言实现与具体实例：
https://blog.csdn.net/appleyuchi/article/details/83863469
EBP剪枝算法的python实现（其实是基于quinlan的ＥＢＰ剪枝的ｐｙｔｈｏｎ接口）：
https://github.com/appleyuchi/Decision_Tree_Prune/tree/master/Quinlan-C4.5-Release8_and_python_interface_for_EBP/
这里有人会质疑为何不直接采用weka中的J48的python接口？
注意，weka是以quinlan的C语言版本代码为准的，在某些数据集中，例如使用hypo数据集，weka的效果是非常糟糕的。
因为决策树的目的是帮助分类，生成知识，
十分庞大的决策树显然是不利于使用的。
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Do we need test sets when pruning?
Attention,here test sets are actually “validation datasets”!

Pruning Algorithm	Need extra test datasets?	Pruning Style
REP(Reduced Error Pruning)	yes	bottom-up
CCP(Cost Complexity Pruning)
ECP(Error Complexity Pruning)
CVP(Critical Value Pruning)
MEP(Minimum Error Pruning)	no	bottom-up
PEP(Pessimistirc Error Pruning)	no	up-bottom
EBP(Error Based Pruning)	no	bottom-up

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