MLcomp version alpha Statistics: 2041 users, 662 datasets, 389 programs, 18865 runs (0 queued, 5 running), 1 workers

Run 16401

Creator	internal
Program	bonzaiboost-n200-d2
Dataset	10K sample run
Task type	MulticlassClassification
Created	319d11h ago

Status: Done!

Total Time
Max Memory Usage
Domain	BinaryClassification
Learn time	1m27s
Train error	0
Predict train time	7s
Test error	0.030
Predict test time	2s

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     1550

0 Go 0 Mo 682 Ko 0 octets
Load round:    0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

Example      === END program2: ./run predict ../program0/evalTest.in ../program0/evalTest.out.multiclass-output --- OK [2s]
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [2s]
=== START program5: ./run evaluate ../dataset3/test ../program0/evalTest.out
=== END program5: ./run evaluate ../dataset3/test ../program0/evalTest.out --- OK [1s]


real	1m37.876s
user	0m53.975s
sys	0m2.212s

Run specification

• supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
  • (learner:Program) binary-to-multi: Allows multiclass classification to be run on binary classification datasets (trivial reduction).
    • (multiclassLearner:Program[MulticlassClassification]) bonzaiboost-n200-d2: 200 turns of AdaBoost.MH over two levels decision trees
  • (dataset:Dataset) 10K sample run:
  • (stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
  • (evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).

Results

• doTest:
  • evaluate:
    • errorRate: 0.0296774193548387
    • numErrors: 46
    • numExamples: 1550
    • success: true
    • time: 1
  • predict:
    • predict:
      • success: true
      • time: 2
    • success: true
    • time: 2
  • strip:
    • success: true
    • time: 1
• doTrain:
  • evaluate:
    • errorRate: 0.0
    • numErrors: 0
    • numExamples: 3616
    • success: true
    • time: 0
  • predict:
    • predict:
      • success: true
      • time: 7
    • success: true
    • time: 7
  • strip:
    • success: true
    • time: 0
• exitCode: 0
• learn:
  • learn:
    • success: true
    • time: 86
  • success: true
  • time: 87
• success: true

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