TABLE 2:

Best test accuracy (%) of MP-Boost, AdaBoost, Random Forest, and Gradient Boosting on binary classification tasks during the runtime of MP-Boost.

Dataset	MP-Boost	AdaBoost	Random Forest	Gradient Boosting
Cones	100.0 ± 0.0	85.56 ± 0.16	95.89 ± 0.97	94.01 ± 1.23
Hill-Valley	99.45 ± 0.19	96.28 ± 1.47	98.76 ± 0.34	96.69 ± 0.67
Christine	74.27 ± 0.32	69.28 ± 0.09	73.75 ± 0.04	70.27 ± 0.13
Jasmine	80.03 ± 0.76	79.53 ± 0.01	79.47 ± 0.55	79.47 ± 0.08
Philippine	71.01 ± 0.37	69.64 ± 0.01	70.07 ± 0.31	69.98 ± 0.01
SensIT Vehicle	86.23 ± 0.01	83.69 ± 0.01	85.98 ± 0.05	84.24 ± 0.02
Higgs Boson	83.42 ± 0.06	82.52 ± 0.01	83.57 ± 0.08	81.44 ± 0.01
MNIST (3,8)	99.31 ± 0.03	97.49 ± 0.08	98.84 ± 0.03	96.81 ± 0.03
MNIST (O,E)	98.15 ± 0.06	93.78 ± 0.01	97.74 ± 0.04	93.23 ± 0.01
CIFAR-10 (T,C)	73.67 ± 0.39	67.65 ± 0.02	73.08 ± 0.4	67.81 ± 0.15
GAS Drift	99.76 ± 0.04	96.54 ± 0.02	99.64 ± 0.08	96.54 ± 0.02
DNA	97.44 ± 0.07	96.86 ± 0.17	97.44 ± 0.2	96.81 ± 0.07
Volkert	74.14 ± 0.08	71.54 ± 0.01	77.95 ± 0.05	71.9 ± 0.04