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Binary classification

Bananas

Summary

Model Accuracy F1 Memory in Mb Time in s
ADWIN Bagging 0.625967 0.448218 0.400658 942.73
ALMA 0.506415 0.482595 0.0029211 68.9731
AdaBoost 0.677864 0.645041 0.453154 876.714
Adaptive Random Forest 0.88696 0.871707 15.3551 2603.02
Aggregated Mondrian Forest 0.889413 0.874249 17.2377 2954.75
Bagging 0.634082 0.459437 0.703124 1170.85
Hoeffding Adaptive Tree 0.616531 0.42825 0.0618467 163.516
Hoeffding Tree 0.642197 0.503405 0.0594654 93.5302
Leveraging Bagging 0.828269 0.802689 3.23571 2747.95
Logistic regression 0.543208 0.197015 0.00424099 82.0689
Naive Bayes 0.61521 0.413912 0.0140247 97.154
Stacking 0.876203 0.859649 19.1946 5236.84
Streaming Random Patches 0.871674 0.854265 10.5381 3551.41
Voting 0.872617 0.849162 4.58403 2790.97
Vowpal Wabbit logistic regression 0.551321 0 0.000646591 88.7248
[baseline] Last Class 0.50953 0.452957 0.000510216 30.809
k-Nearest Neighbors 0.885073 0.870838 4.50996 2974.33
sklearn SGDClassifier 0.546415 0.205026 0.00557804 621.426

Charts

Elec2

Summary

Model Accuracy F1 Memory in Mb Time in s
ADWIN Bagging 0.823773 0.776587 0.598438 8970.15
ALMA 0.906427 0.889767 0.00435829 836.498
AdaBoost 0.880581 0.858687 13.5424 10153.7
Adaptive Random Forest 0.876608 0.852391 22.3949 12397.6
Aggregated Mondrian Forest 0.849904 0.819731 287.315 18206.6
Bagging 0.840436 0.80208 2.28896 13164.5
Hoeffding Adaptive Tree 0.821258 0.787344 0.435328 2980.69
Hoeffding Tree 0.795635 0.750834 0.938466 1485.98
Leveraging Bagging 0.892653 0.871966 7.56535 18763.3
Logistic regression 0.822144 0.777086 0.005373 953.54
Naive Bayes 0.728741 0.603785 0.0510378 1230.66
Stacking 0.885458 0.864157 40.7547 22944.4
Streaming Random Patches 0.868884 0.843009 107.322 22969
Voting 0.84368 0.797958 5.7575 13925.5
Vowpal Wabbit logistic regression 0.697475 0.459592 0.000646591 937.011
[baseline] Last Class 0.853303 0.827229 0.000510216 341.39
k-Nearest Neighbors 0.853148 0.823642 4.76604 13503.4
sklearn SGDClassifier 0.819099 0.772892 0.00680161 4291.77

Charts

Phishing

Summary

Model Accuracy F1 Memory in Mb Time in s
ADWIN Bagging 0.893515 0.879201 1.31008 568.218
ALMA 0.8256 0.810764 0.0045805 29.7613
AdaBoost 0.878303 0.863555 0.873312 552.609
Adaptive Random Forest 0.907926 0.896116 4.10291 743.377
Aggregated Mondrian Forest 0.904724 0.892112 3.39106 807.573
Bagging 0.893515 0.879201 1.38826 633.136
Hoeffding Adaptive Tree 0.874299 0.856095 0.142962 77.865
Hoeffding Tree 0.879904 0.860595 0.132719 54.2758
Leveraging Bagging 0.894315 0.877323 4.0114 1619.65
Logistic regression 0.8872 0.871233 0.00556469 29.2066
Naive Bayes 0.884708 0.871429 0.05723 38.528
Stacking 0.895116 0.882722 8.72124 2411.41
Streaming Random Patches 0.913531 0.901996 6.59559 1436.69
Voting 0.896717 0.884512 4.8203 1436.72
Vowpal Wabbit logistic regression 0.7736 0.669778 0.000646591 27.8334
[baseline] Last Class 0.515612 0.447489 0.000510216 11.9196
k-Nearest Neighbors 0.881505 0.867145 4.59643 1552.65
sklearn SGDClassifier 0.8896 0.876122 0.00701618 167.984

Charts

SMTP

Summary

Model Accuracy F1 Memory in Mb Time in s
ADWIN Bagging 0.999685 0 0.164217 8006.78
ALMA 0.764986 0.00178548 0.00309372 1361.61
AdaBoost 0.999443 0.404494 1.33633 6617.5
Adaptive Random Forest 0.999685 0 0.327095 11543.4
Aggregated Mondrian Forest 0.999863 0.734694 0.211749 5848.87
Bagging 0.999685 0 0.207971 8814.84
Hoeffding Adaptive Tree 0.999685 0 0.0241137 2094.95
Hoeffding Tree 0.999685 0 0.0170441 1543.56
Leveraging Bagging 0.999674 0 0.164603 17549.6
Logistic regression 0.999769 0.421053 0.00438309 1531.37
Naive Bayes 0.993484 0.0490798 0.0201406 1826.47
Stacking 0.999685 0 4.88868 24733.2
Streaming Random Patches 0.999685 0 0.17817 18142.3
Voting 0.999779 0.487805 4.60205 18069.8
Vowpal Wabbit logistic regression 0.999695 0.121212 0.000646591 1631.37
[baseline] Last Class 0.999601 0.366667 0.000510216 532.359
k-Nearest Neighbors 0.999821 0.666667 4.51822 17961.1
sklearn SGDClassifier 0.999706 0.363636 0.00574303 7118.18

Charts