.TH C4.5 1.SH NAMEA guide to the verbose output of the C4.5 production rule generator.SH DESCRIPTIONThis document explains the output of the program.I C4.5ruleswhen it is runwith the verbosity level (option.BR v )set to values from 1 to 3..I C4.5rulesconverts unpruned decision trees into sets of pruned productionrules.  Each set of rules is then sifted to find a subset of therules which perform as well or better on the training data (see.IR c4.5rules(1) )..SH RULE PRUNING.B Verbosity level 1A decision tree is converted to a set of production rulesby forming a rule corresponding to each path from theroot of the tree to each of its leaves.After each rule is extracted from the tree, it is examinedto see whether the rule can be generalised by droppingconditions.For each rule, the verbose output shows the following figuresfor the rule as it stands, and for each of the rules that wouldbe formed by dropping any one of the conditions:        Miss - no. of items misclassified by the rule        Hit  - no. of items correctly classified by the rule        Pess - the pessimistic error rate of the rule                 (i.e. 100*(misses+1)/(misses+hits+2))        Gain - the information gain of the rule        Absent condition - the condition being ignoredIf there are any conditions whose deletion brings about rules withpessimistic error rate less than the default error rate,and gain greater than that of the rule as it stands,then the one of these with the lowest pessimistic error rateis dropped.  When this happens, the message:	eliminate test \fId\fRis given and the new rule without condition \fId\fRis examined, and so on.When the rule has been pruned, either the rule is displayed,or the message:	duplicates rule \fIn\fRis given, where \fIn\fR is an identical rule already produced,and so the new rule is not added, or the message:	too inaccurateis given, indicating that the pessimistic error rate of thepruned rule is more than 50%, or more than the proportion ofthe items that are of the rule's class, and so the rule isnot added..SH RULE SIFTING.B Verbosity level 1The set of pruned rules for each class is then examined.Starting with no rules in the ruleset, the followingprocess is repeated until no rules can be added or dropped..IP "    1." 7If there are rules whose omission would not leadto an increase in the number of items misclassified,then the least useful of these is dropped..IP "    2."Otherwise, if there are rules which lead to a decreasein the number of items misclassified, then the onewith the least counterexamples is added..TP 0This is shown in the output as:    Action  -  the number of the rule added or dropped    Change  -  the advantage attributable to the rule    Worth   -  the included rules for this class as:.IR                n1 [ n2 | n3 =.IR r1 ]    with:.IP "        \fIn1\fR" 11- the rule number.IP "        \fIn2\fR"- the number of items that correctlyfire this rule and are not covered by any other included rule.IP "        \fIn3\fR"- the number of items that incorrectlyfire this rule and are not covered by any other included rule.IP "        \fIr1\fR- the advantage attributable to therule.HP 0After the rules have been sifted, the number of items ofeach class that are not covered by any rules is shown,and the default class is set to the class with the mostuncovered items..B Verbosity level 2When sifting rules for a particular class, the Worth of each rulewhich is for that class but not included in the ruleset, is shown at each stage of the process..SH RULE SORTING.B Verbosity level 1The rules that are left are then sorted, starting with thosethat are for the class with the least number of false positives.The verbose output shows the number of false positives for eachclass (i.e. the number of items misclassified as being of thisclass).Within a class, rules with the greatest advantage are put first..SH RULESET EVALUATION.B Verbosity level 3When evaluating a ruleset, shown are the attribute values,given class and class given by the ruleset for eachitem that is misclassified..SH SEE ALSOc4.5(1), c4.5rules(1)