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Table F.2.

Optimal augmented representations and random forest hyperparameters that yielded the best validation macro recalls (RMV) on the synthetic samples.

Sample Representation n_estimators min_samples_leaf max_features RDCV RSDV RCV RMV RDCT RSDT RCT RMT
W0C0 C ∼ F+V 8 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{8} $ 100 1 sqrt 0.97 0.99 0.98 0.98 0.96 0.97 0.97 0.97
W0C1L25 C ∼ SD+V 8 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{8} $ 500 1 sqrt 0.90 0.89 0.97 0.92 0.87 0.86 0.95 0.89
W0C1L50 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 1 sqrt 0.93 0.92 0.96 0.93 0.90 0.90 0.95 0.92
W0C1L100 C ∼ F+V 9 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{9} $ 500 1 sqrt 0.96 0.97 0.98 0.97 0.96 0.97 0.96 0.96
W0C5L25 C ∼ C+V 5 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{5} $ 100 10 sqrt 0.85 0.74 0.84 0.81 0.81 0.68 0.81 0.77
W0C5L50 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 10 sqrt 0.84 0.76 0.86 0.82 0.81 0.71 0.82 0.78
W0C5L100 C ∼ F+V 9 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{9} $ 500 1 sqrt 0.90 0.90 0.94 0.91 0.86 0.87 0.92 0.88
W0C10L25 C ∼ C+V 7 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{7} $ 500 10 sqrt 0.79 0.67 0.74 0.73 0.77 0.61 0.69 0.69
W0C10L50 C ∼ C+V 9 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{9} $ 100 10 sqrt 0.76 0.70 0.72 0.73 0.74 0.64 0.70 0.69
W0C10L100 C ∼ LC 99 $ \boldsymbol{\tilde{C}}^\text{ LC}_{99} $ 100 10 sqrt 0.88 0.76 0.91 0.85 0.84 0.73 0.89 0.82
W1C0 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 1 sqrt 0.96 0.95 0.98 0.97 0.94 0.95 0.97 0.95
W1C1L25 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 1 sqrt 0.90 0.89 0.96 0.92 0.86 0.87 0.94 0.89
W1C1L50 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 1 sqrt 0.92 0.91 0.97 0.93 0.91 0.91 0.95 0.92
W1C1L100 C ∼ F+V 9 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{9} $ 500 1 sqrt 0.95 0.95 0.98 0.96 0.95 0.93 0.96 0.95
W1C5L25 C ∼ F+V 7 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{7} $ 500 10 sqrt 0.85 0.73 0.85 0.81 0.83 0.67 0.81 0.77
W1C5L50 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 10 None 0.83 0.76 0.85 0.81 0.81 0.72 0.81 0.78
W1C5L100 C ∼ F+V 9 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{9} $ 500 1 sqrt 0.90 0.89 0.92 0.90 0.85 0.86 0.89 0.87
W1C10L25 C ∼ F+V 9 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{9} $ 500 10 sqrt 0.79 0.67 0.73 0.73 0.78 0.61 0.69 0.69
W1C10L50 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 10 sqrt 0.77 0.69 0.74 0.73 0.74 0.64 0.68 0.69
W1C10L100 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 100 10 None 0.87 0.80 0.87 0.84 0.83 0.75 0.82 0.80
W10C0 C ∼ SD+V 8 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{8} $ 500 1 sqrt 0.91 0.89 0.96 0.92 0.89 0.87 0.92 0.90
W10C1L25 C ∼ SD+V 4 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{4} $ 500 10 sqrt 0.92 0.82 0.96 0.90 0.89 0.79 0.92 0.87
W10C1L50 C ∼ SD+V 8 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{8} $ 100 1 sqrt 0.89 0.87 0.94 0.90 0.86 0.85 0.92 0.88
W10C1L100 C ∼ SD+V 8 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{8} $ 500 1 sqrt 0.89 0.88 0.95 0.91 0.88 0.86 0.92 0.89
W10C5L25 C ∼ C+V 5 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{5} $ 500 10 sqrt 0.86 0.73 0.85 0.81 0.83 0.67 0.79 0.76
W10C5L50 C ∼ SD+V 9 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{9} $ 500 10 sqrt 0.84 0.76 0.84 0.81 0.82 0.70 0.81 0.78
W10C5L100 C ∼ SD+V 8 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{8} $ 100 10 sqrt 0.87 0.80 0.91 0.86 0.84 0.76 0.87 0.83
W10C10L25 C ∼ F+V 8 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{8} $ 100 10 sqrt 0.77 0.68 0.73 0.72 0.75 0.62 0.69 0.69
W10C10L50 C ∼ SD+V 7 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{7} $ 100 10 sqrt 0.76 0.70 0.74 0.74 0.75 0.64 0.69 0.69
W10C10L100 C ∼ SD+V 8 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{8} $ 500 10 None 0.84 0.75 0.85 0.81 0.82 0.71 0.81 0.78
W100C0 C ∼ F+V 8 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{8} $ 500 10 sqrt 0.89 0.74 0.81 0.81 0.88 0.69 0.78 0.78
W100C1L25 C ∼ F+V 8 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{8} $ 500 10 None 0.87 0.74 0.82 0.81 0.85 0.70 0.77 0.77
W100C1L50 C ∼ SD+V 4 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{4} $ 500 10 sqrt 0.90 0.74 0.81 0.82 0.86 0.68 0.76 0.77
W100C1L100 C ∼ F+V 8 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{8} $ 100 10 sqrt 0.89 0.74 0.81 0.81 0.87 0.69 0.77 0.78
W100C5L25 C ∼ C+V 5 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{5} $ 100 10 None 0.84 0.71 0.78 0.77 0.81 0.65 0.73 0.73
W100C5L50 C ∼ SD+V 7 $ \boldsymbol{\tilde{C}}^\text{ SD+V}_{7} $ 100 10 sqrt 0.82 0.72 0.76 0.77 0.79 0.67 0.73 0.73
W100C5L100 C ∼ C+V 7 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{7} $ 500 10 None 0.82 0.73 0.79 0.78 0.79 0.68 0.75 0.74
W100C10L25 C ∼ F+V 7 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{7} $ 500 10 sqrt 0.79 0.68 0.68 0.72 0.77 0.60 0.64 0.67
W100C10L50 C ∼ C+V 7 $ \boldsymbol{\tilde{C}}^\text{ C+V}_{7} $ 100 10 None 0.78 0.69 0.68 0.72 0.76 0.62 0.65 0.68
W100C10L100 C ∼ F+V 8 $ \boldsymbol{\tilde{C}}^\text{ F+V}_{8} $ 500 10 sqrt 0.80 0.71 0.75 0.75 0.78 0.66 0.70 0.72

Notes. We also provide the validation class recalls for the dark companion (RDCV), semidetached (RSDV), and contact (RCV) binary light curves as well as the test class recalls (RDCT, RSDT, RCT) and the test macro recall (RMT) of the best performing classifiers. See Sect. 2 for the description of the synthetic samples and Sect. 3.4 for the definitions of the augmented representations and the hyperparameters. The symbol C ∼ LC 99 $ \boldsymbol{\tilde{C}}^\text{ LC}_{99} $ denotes the extended full representation.

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