1 | .TH C4.5 1 |
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2 | .SH NAME |
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3 | A guide to the verbose output of the C4.5 production rule generator |
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4 | |
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5 | .SH DESCRIPTION |
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6 | This document explains the output of the program |
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7 | .I C4.5rules |
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8 | when it is run |
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9 | with the verbosity level (option |
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10 | .BR v ) |
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11 | set to values from 1 to 3. |
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12 | .I C4.5rules |
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13 | converts unpruned decision trees into sets of pruned production |
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14 | rules. Each set of rules is then sifted to find a subset of the |
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15 | rules which perform as well or better on the training data (see |
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16 | .IR c4.5rules(1) ). |
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17 | |
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18 | .SH RULE PRUNING |
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19 | |
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20 | .B Verbosity level 1 |
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21 | |
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22 | A decision tree is converted to a set of production rules |
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23 | by forming a rule corresponding to each path from the |
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24 | root of the tree to each of its leaves. |
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25 | After each rule is extracted from the tree, it is examined |
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26 | to see whether the rule can be generalised by dropping |
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27 | conditions. |
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28 | |
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29 | For each rule, the verbose output shows the following figures |
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30 | for the rule as it stands, and for each of the rules that would |
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31 | be formed by dropping any one of the conditions: |
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32 | |
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33 | Miss - no. of items misclassified by the rule |
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34 | Hit - no. of items correctly classified by the rule |
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35 | Pess - the pessimistic error rate of the rule |
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36 | (i.e. 100*(misses+1)/(misses+hits+2)) |
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37 | Gain - the information gain of the rule |
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38 | Absent condition - the condition being ignored |
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39 | |
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40 | If there are any conditions whose deletion brings about rules with |
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41 | pessimistic error rate less than the default error rate, |
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42 | and gain greater than that of the rule as it stands, |
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43 | then the one of these with the lowest pessimistic error rate |
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44 | is dropped. When this happens, the message: |
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45 | |
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46 | eliminate test \fId\fR |
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47 | |
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48 | is given and the new rule without condition \fId\fR |
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49 | is examined, and so on. |
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50 | |
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51 | When the rule has been pruned, either the rule is displayed, |
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52 | or the message: |
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53 | |
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54 | duplicates rule \fIn\fR |
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55 | |
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56 | is given, where \fIn\fR is an identical rule already produced, |
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57 | and so the new rule is not added, or the message: |
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58 | |
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59 | too inaccurate |
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60 | |
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61 | is given, indicating that the pessimistic error rate of the |
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62 | pruned rule is more than 50%, or more than the proportion of |
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63 | the items that are of the rule's class, and so the rule is |
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64 | not added. |
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65 | |
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66 | |
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67 | .SH RULE SIFTING |
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68 | |
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69 | .B Verbosity level 1 |
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70 | |
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71 | The set of pruned rules for each class is then examined. |
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72 | Starting with no rules in the ruleset, the following |
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73 | process is repeated until no rules can be added or dropped. |
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74 | .IP " 1." 7 |
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75 | If there are rules whose omission would not lead |
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76 | to an increase in the number of items misclassified, |
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77 | then the least useful of these is dropped. |
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78 | .IP " 2." |
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79 | Otherwise, if there are rules which lead to a decrease |
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80 | in the number of items misclassified, then the one |
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81 | with the least counterexamples is added. |
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82 | .TP 0 |
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83 | This is shown in the output as: |
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84 | |
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85 | Action - the number of the rule added or dropped |
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86 | Change - the advantage attributable to the rule |
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87 | Worth - the included rules for this class as: |
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88 | |
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89 | .IR n1 [ n2 | n3 = |
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90 | .IR r1 ] |
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91 | |
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92 | with: |
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93 | .IP " \fIn1\fR" 11 |
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94 | - the rule number |
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95 | .IP " \fIn2\fR" |
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96 | - the number of items that correctly |
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97 | fire this rule and are not covered by any other included rule |
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98 | .IP " \fIn3\fR" |
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99 | - the number of items that incorrectly |
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100 | fire this rule and are not covered by any other included rule |
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101 | .IP " \fIr1\fR |
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102 | - the advantage attributable to the |
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103 | rule |
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104 | .HP 0 |
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105 | After the rules have been sifted, the number of items of |
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106 | each class that are not covered by any rules is shown, |
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107 | and the default class is set to the class with the most |
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108 | uncovered items. |
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109 | |
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110 | |
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111 | .B Verbosity level 2 |
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112 | |
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113 | When sifting rules for a particular class, the Worth of each rule |
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114 | which is for that class but not included in the ruleset, |
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115 | is shown at each stage of the process. |
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116 | |
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117 | .SH RULE SORTING |
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118 | |
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119 | .B Verbosity level 1 |
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120 | |
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121 | The rules that are left are then sorted, starting with those |
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122 | that are for the class with the least number of false positives. |
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123 | The verbose output shows the number of false positives for each |
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124 | class (i.e. the number of items misclassified as being of this |
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125 | class). |
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126 | Within a class, rules with the greatest advantage are put first. |
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127 | |
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128 | .SH RULESET EVALUATION |
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129 | |
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130 | .B Verbosity level 3 |
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131 | |
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132 | When evaluating a ruleset, shown are the attribute values, |
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133 | given class and class given by the ruleset for each |
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134 | item that is misclassified. |
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135 | |
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136 | |
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137 | .SH SEE ALSO |
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138 | |
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139 | c4.5(1), c4.5rules(1) |
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