RandomForestClassifier RandomizedSearchCV
pistacja96 edited this page Feb 5, 2018
·
6 revisions
Parameter optimization via CV for n_trees = 250,
Model with rank: 1 Validation accuracy: 0.83135 Parameters: {max_depth = 144, min_samples_split = 80}
Model with rank: 2 Validation accuracy: 0.8305 Parameters: {max_depth = 144, min_samples_split = 87}
Model with rank: 3 Validation accuracy: 0.82985 Parameters: {max_depth = 132, min_samples_split = 80}
Parameters: [max_depth=144, min_samples_split=81], validation accuracy = 0.82965
Parameters: [max_depth=144, min_samples_split=83], validation accuracy = 0.82715
Parameters: [max_depth=144, min_samples_split=85], validation accuracy = 0.82805
Parameters: [max_depth=144, min_samples_split=87], validation accuracy = 0.8305
Parameters: [max_depth=144, min_samples_split=89], validation accuracy = 0.828
Parameters: [max_depth=120, min_samples_split=80], validation accuracy = 0.8285
Parameters: [max_depth=120, min_samples_split=100], validation accuracy = 0.82865
Parameters: [max_depth=122, min_samples_split=80], validation accuracy = 0.8276
Parameters: [max_depth=122, min_samples_split=100], validation accuracy = 0.8279
Parameters: [max_depth=124, min_samples_split=80], validation accuracy = 0.8266
Parameters: [max_depth=124, min_samples_split=100], validation accuracy = 0.828
Parameters: [max_depth=126, min_samples_split=80], validation accuracy = 0.82665
Parameters: [max_depth=126, min_samples_split=100], validation accuracy = 0.82885
Parameters: [max_depth=128, min_samples_split=80], validation accuracy = 0.8298
Parameters: [max_depth=128, min_samples_split=100], validation accuracy = 0.82745
Parameters: [max_depth=128, min_samples_split=80], validation accuracy = 0.8289
Parameters: [max_depth=130, min_samples_split=80], validation accuracy = 0.82855
Parameters: [max_depth=132, min_samples_split=80], validation accuracy = 0.82985
Parameters: [max_depth=134, min_samples_split=80], validation accuracy = 0.8296
Parameters: [max_depth=136, min_samples_split=80], validation accuracy = 0.8279
Parameters: [max_depth=138, min_samples_split=80], validation accuracy = 0.82785
Parameters: [max_depth=140, min_samples_split=80], validation accuracy = 0.82925
Parameters: [max_depth=142, min_samples_split=80], validation accuracy = 0.82815
Parameters: [max_depth=144, min_samples_split=80], validation accuracy = 0.83135
Parameters: [max_depth=146, min_samples_split=80], validation accuracy = 0.82845
After parameter optimization via CV for n_trees = 10,
Model with rank: 1 Mean validation score: 0.815 (std: 0.000) Parameters: {'min_samples_leaf': 2, 'min_samples_split': 178}
Model with rank: 2 Mean validation score: 0.814 (std: 0.001) Parameters: {'min_samples_leaf': 1, 'min_samples_split': 130}
Model with rank: 3 Mean validation score: 0.814 (std: 0.001) Parameters: {'min_samples_leaf': 2, 'min_samples_split': 156}
Randomize CV over bootstrap, criterion, max_features, min_samples_leaf, min_samples_split. Eliminate criterion & bootstrap, doesn't matter that much. Eliminate None from max_features, takes ~50-100x longer to train and performs worse.
[mean: 0.66250, std: 0.01698, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': 'log2', 'min_samples_leaf': 192, 'min_samples_split': 194},
mean: 0.67735, std: 0.02604, params: {'bootstrap': True, 'criterion': 'gini', 'max_features': 'log2', 'min_samples_leaf': 182, 'min_samples_split': 66},
mean: 0.70545, std: 0.01355, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': 'log2', 'min_samples_leaf': 146, 'min_samples_split': 103},
mean: 0.71070, std: 0.01155, params: {'bootstrap': False, 'criterion': 'gini', 'max_features': 'log2', 'min_samples_leaf': 198, 'min_samples_split': 20},
mean: 0.72515, std: 0.00604, params: {'bootstrap': False, 'criterion': 'gini', 'max_features': None, 'min_samples_leaf': 158, 'min_samples_split': 30},
mean: 0.72625, std: 0.00754, params: {'bootstrap': False, 'criterion': 'gini', 'max_features': None, 'min_samples_leaf': 155, 'min_samples_split': 53},
mean: 0.73865, std: 0.00155, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': None, 'min_samples_leaf': 111, 'min_samples_split': 10},
mean: 0.74360, std: 0.01173, params: {'bootstrap': True, 'criterion': 'gini', 'max_features': 'sqrt', 'min_samples_leaf': 184, 'min_samples_split': 79},
mean: 0.74890, std: 0.00499, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': 'log2', 'min_samples_leaf': 73, 'min_samples_split': 33},
mean: 0.74925, std: 0.00389, params: {'bootstrap': False, 'criterion': 'gini', 'max_features': None, 'min_samples_leaf': 49, 'min_samples_split': 149},
mean: 0.75035, std: 0.00784, params: {'bootstrap': False, 'criterion': 'entropy', 'max_features': None, 'min_samples_leaf': 8, 'min_samples_split': 56},
mean: 0.75555, std: 0.00578, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': 'sqrt', 'min_samples_leaf': 162, 'min_samples_split': 103},
mean: 0.76430, std: 0.00631, params: {'bootstrap': False, 'criterion': 'gini', 'max_features': 'sqrt', 'min_samples_leaf': 139, 'min_samples_split': 118},
mean: 0.76470, std: 0.00273, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': None, 'min_samples_leaf': 34, 'min_samples_split': 32},
mean: 0.76965, std: 0.00401, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': 'sqrt', 'min_samples_leaf': 108, 'min_samples_split': 155},
mean: 0.76970, std: 0.00304, params: {'bootstrap': False, 'criterion': 'entropy', 'max_features': 'sqrt', 'min_samples_leaf': 141, 'min_samples_split': 182},
mean: 0.77720, std: 0.00555, params: {'bootstrap': True, 'criterion': 'entropy', 'max_features': 'sqrt', 'min_samples_leaf': 55, 'min_samples_split': 139},
mean: 0.77900, std: 0.00312, params: {'bootstrap': False, 'criterion': 'gini', 'max_features': 'log2', 'min_samples_leaf': 55, 'min_samples_split': 125},
mean: 0.78125, std: 0.00212, params: {'bootstrap': False, 'criterion': 'entropy', 'max_features': 'sqrt', 'min_samples_leaf': 86, 'min_samples_split': 21},
mean: 0.79895, std: 0.00345, params: {'bootstrap': True, 'criterion': 'gini', 'max_features': 'log2', 'min_samples_leaf': 4, 'min_samples_split': 45}]End up with sqrt for max_features, min_samples_leaf < 10, optimize for min_samples_split.
[mean: 0.67180, std: 0.00807, params: {'max_features': 'log2', 'min_samples_leaf': 183, 'min_samples_split': 173},
mean: 0.67640, std: 0.01743, params: {'max_features': 'log2', 'min_samples_leaf': 171, 'min_samples_split': 139},
mean: 0.68670, std: 0.01246, params: {'max_features': 'log2', 'min_samples_leaf': 148, 'min_samples_split': 137},
mean: 0.69275, std: 0.00656, params: {'max_features': 'log2', 'min_samples_leaf': 167, 'min_samples_split': 121},
mean: 0.69805, std: 0.00601, params: {'max_features': 'log2', 'min_samples_leaf': 152, 'min_samples_split': 194},
mean: 0.70205, std: 0.02348, params: {'max_features': 'log2', 'min_samples_leaf': 151, 'min_samples_split': 132},
mean: 0.73030, std: 0.01065, params: {'max_features': 'sqrt', 'min_samples_leaf': 185, 'min_samples_split': 38},
mean: 0.73635, std: 0.00784, params: {'max_features': 'sqrt', 'min_samples_leaf': 193, 'min_samples_split': 137},
mean: 0.76260, std: 0.00558, params: {'max_features': 'log2', 'min_samples_leaf': 60, 'min_samples_split': 59},
mean: 0.76680, std: 0.00690, params: {'max_features': 'sqrt', 'min_samples_leaf': 80, 'min_samples_split': 169},
mean: 0.76710, std: 0.01010, params: {'max_features': 'sqrt', 'min_samples_leaf': 111, 'min_samples_split': 194},
mean: 0.76730, std: 0.01427, params: {'max_features': 'log2', 'min_samples_leaf': 53, 'min_samples_split': 177},
mean: 0.76815, std: 0.00589, params: {'max_features': 'sqrt', 'min_samples_leaf': 94, 'min_samples_split': 31},
mean: 0.77280, std: 0.00073, params: {'max_features': 'sqrt', 'min_samples_leaf': 110, 'min_samples_split': 27},
mean: 0.77510, std: 0.01050, params: {'max_features': 'log2', 'min_samples_leaf': 34, 'min_samples_split': 74},
mean: 0.77750, std: 0.00408, params: {'max_features': 'sqrt', 'min_samples_leaf': 66, 'min_samples_split': 66},
mean: 0.78715, std: 0.00240, params: {'max_features': 'sqrt', 'min_samples_leaf': 33, 'min_samples_split': 176},
mean: 0.78955, std: 0.00338, params: {'max_features': 'sqrt', 'min_samples_leaf': 19, 'min_samples_split': 55},
mean: 0.79160, std: 0.00134, params: {'max_features': 'sqrt', 'min_samples_leaf': 24, 'min_samples_split': 86},
mean: 0.80195, std: 0.00706, params: {'max_features': 'sqrt', 'min_samples_leaf': 6, 'min_samples_split': 87}]min_samples_leaf = 2, min_samples_split = 178.
[mean: 0.80305, std: 0.00188, params: {'min_samples_leaf': 9, 'min_samples_split': 180},
mean: 0.80310, std: 0.00436, params: {'min_samples_leaf': 9, 'min_samples_split': 168},
mean: 0.80360, std: 0.00185, params: {'min_samples_leaf': 1, 'min_samples_split': 92},
mean: 0.80400, std: 0.00304, params: {'min_samples_leaf': 8, 'min_samples_split': 120},
mean: 0.80440, std: 0.00154, params: {'min_samples_leaf': 9, 'min_samples_split': 129},
mean: 0.80455, std: 0.00111, params: {'min_samples_leaf': 9, 'min_samples_split': 196},
mean: 0.80475, std: 0.00517, params: {'min_samples_leaf': 8, 'min_samples_split': 82},
mean: 0.80490, std: 0.00039, params: {'min_samples_leaf': 9, 'min_samples_split': 175},
mean: 0.80530, std: 0.00312, params: {'min_samples_leaf': 8, 'min_samples_split': 170},
mean: 0.80540, std: 0.00098, params: {'min_samples_leaf': 9, 'min_samples_split': 63},
mean: 0.80540, std: 0.00263, params: {'min_samples_leaf': 7, 'min_samples_split': 42},
mean: 0.80550, std: 0.00339, params: {'min_samples_leaf': 9, 'min_samples_split': 11},
mean: 0.80565, std: 0.00063, params: {'min_samples_leaf': 7, 'min_samples_split': 81},
mean: 0.80595, std: 0.00167, params: {'min_samples_leaf': 8, 'min_samples_split': 15},
mean: 0.80600, std: 0.00074, params: {'min_samples_leaf': 8, 'min_samples_split': 47},
mean: 0.80600, std: 0.00271, params: {'min_samples_leaf': 6, 'min_samples_split': 114},
mean: 0.80605, std: 0.00214, params: {'min_samples_leaf': 9, 'min_samples_split': 183},
mean: 0.80610, std: 0.00174, params: {'min_samples_leaf': 9, 'min_samples_split': 72},
mean: 0.80615, std: 0.00333, params: {'min_samples_leaf': 7, 'min_samples_split': 132},
mean: 0.80635, std: 0.00373, params: {'min_samples_leaf': 9, 'min_samples_split': 99},
mean: 0.80635, std: 0.00349, params: {'min_samples_leaf': 5, 'min_samples_split': 29},
mean: 0.80640, std: 0.00397, params: {'min_samples_leaf': 7, 'min_samples_split': 98},
mean: 0.80645, std: 0.00222, params: {'min_samples_leaf': 9, 'min_samples_split': 67},
mean: 0.80650, std: 0.00300, params: {'min_samples_leaf': 9, 'min_samples_split': 99},
mean: 0.80655, std: 0.00071, params: {'min_samples_leaf': 7, 'min_samples_split': 121},
mean: 0.80655, std: 0.00093, params: {'min_samples_leaf': 5, 'min_samples_split': 16},
mean: 0.80655, std: 0.00105, params: {'min_samples_leaf': 1, 'min_samples_split': 13},
mean: 0.80660, std: 0.00391, params: {'min_samples_leaf': 8, 'min_samples_split': 24},
mean: 0.80670, std: 0.00168, params: {'min_samples_leaf': 6, 'min_samples_split': 198},
mean: 0.80670, std: 0.00231, params: {'min_samples_leaf': 6, 'min_samples_split': 127},
mean: 0.80675, std: 0.00293, params: {'min_samples_leaf': 7, 'min_samples_split': 55},
mean: 0.80680, std: 0.00055, params: {'min_samples_leaf': 1, 'min_samples_split': 32},
mean: 0.80690, std: 0.00338, params: {'min_samples_leaf': 8, 'min_samples_split': 68},
mean: 0.80690, std: 0.00244, params: {'min_samples_leaf': 9, 'min_samples_split': 69},
mean: 0.80690, std: 0.00306, params: {'min_samples_leaf': 9, 'min_samples_split': 103},
mean: 0.80695, std: 0.00250, params: {'min_samples_leaf': 9, 'min_samples_split': 99},
mean: 0.80695, std: 0.00491, params: {'min_samples_leaf': 9, 'min_samples_split': 119},
mean: 0.80700, std: 0.00063, params: {'min_samples_leaf': 6, 'min_samples_split': 38},
mean: 0.80705, std: 0.00248, params: {'min_samples_leaf': 8, 'min_samples_split': 126},
mean: 0.80710, std: 0.00404, params: {'min_samples_leaf': 7, 'min_samples_split': 68},
mean: 0.80710, std: 0.00255, params: {'min_samples_leaf': 5, 'min_samples_split': 45},
mean: 0.80730, std: 0.00492, params: {'min_samples_leaf': 1, 'min_samples_split': 186},
mean: 0.80730, std: 0.00341, params: {'min_samples_leaf': 2, 'min_samples_split': 11},
mean: 0.80730, std: 0.00343, params: {'min_samples_leaf': 7, 'min_samples_split': 97},
mean: 0.80735, std: 0.00166, params: {'min_samples_leaf': 8, 'min_samples_split': 91},
mean: 0.80735, std: 0.00279, params: {'min_samples_leaf': 6, 'min_samples_split': 187},
mean: 0.80735, std: 0.00483, params: {'min_samples_leaf': 7, 'min_samples_split': 166},
mean: 0.80740, std: 0.00233, params: {'min_samples_leaf': 3, 'min_samples_split': 174},
mean: 0.80750, std: 0.00558, params: {'min_samples_leaf': 6, 'min_samples_split': 187},
mean: 0.80750, std: 0.00377, params: {'min_samples_leaf': 7, 'min_samples_split': 164},
mean: 0.80755, std: 0.00260, params: {'min_samples_leaf': 5, 'min_samples_split': 65},
mean: 0.80765, std: 0.00134, params: {'min_samples_leaf': 1, 'min_samples_split': 16},
mean: 0.80765, std: 0.00309, params: {'min_samples_leaf': 9, 'min_samples_split': 104},
mean: 0.80775, std: 0.00029, params: {'min_samples_leaf': 4, 'min_samples_split': 142},
mean: 0.80780, std: 0.00374, params: {'min_samples_leaf': 4, 'min_samples_split': 10},
mean: 0.80780, std: 0.00103, params: {'min_samples_leaf': 4, 'min_samples_split': 114},
mean: 0.80790, std: 0.00675, params: {'min_samples_leaf': 8, 'min_samples_split': 168},
mean: 0.80790, std: 0.00374, params: {'min_samples_leaf': 3, 'min_samples_split': 79},
mean: 0.80790, std: 0.00133, params: {'min_samples_leaf': 8, 'min_samples_split': 37},
mean: 0.80795, std: 0.00429, params: {'min_samples_leaf': 5, 'min_samples_split': 80},
mean: 0.80795, std: 0.00569, params: {'min_samples_leaf': 4, 'min_samples_split': 185},
mean: 0.80795, std: 0.00214, params: {'min_samples_leaf': 9, 'min_samples_split': 57},
mean: 0.80800, std: 0.00110, params: {'min_samples_leaf': 1, 'min_samples_split': 40},
mean: 0.80800, std: 0.00180, params: {'min_samples_leaf': 5, 'min_samples_split': 25},
mean: 0.80800, std: 0.00519, params: {'min_samples_leaf': 7, 'min_samples_split': 39},
mean: 0.80805, std: 0.00503, params: {'min_samples_leaf': 7, 'min_samples_split': 135},
mean: 0.80805, std: 0.00339, params: {'min_samples_leaf': 6, 'min_samples_split': 29},
mean: 0.80805, std: 0.00210, params: {'min_samples_leaf': 3, 'min_samples_split': 86},
mean: 0.80820, std: 0.00221, params: {'min_samples_leaf': 5, 'min_samples_split': 88},
mean: 0.80820, std: 0.00073, params: {'min_samples_leaf': 9, 'min_samples_split': 199},
mean: 0.80825, std: 0.00109, params: {'min_samples_leaf': 8, 'min_samples_split': 177},
mean: 0.80840, std: 0.00139, params: {'min_samples_leaf': 9, 'min_samples_split': 36},
mean: 0.80840, std: 0.00385, params: {'min_samples_leaf': 5, 'min_samples_split': 165},
mean: 0.80840, std: 0.00270, params: {'min_samples_leaf': 7, 'min_samples_split': 38},
mean: 0.80840, std: 0.00306, params: {'min_samples_leaf': 3, 'min_samples_split': 176},
mean: 0.80840, std: 0.00155, params: {'min_samples_leaf': 3, 'min_samples_split': 184},
mean: 0.80845, std: 0.00136, params: {'min_samples_leaf': 2, 'min_samples_split': 177},
mean: 0.80850, std: 0.00156, params: {'min_samples_leaf': 5, 'min_samples_split': 143},
mean: 0.80855, std: 0.00279, params: {'min_samples_leaf': 7, 'min_samples_split': 25},
mean: 0.80855, std: 0.00404, params: {'min_samples_leaf': 9, 'min_samples_split': 178},
mean: 0.80855, std: 0.00315, params: {'min_samples_leaf': 1, 'min_samples_split': 77},
mean: 0.80875, std: 0.00359, params: {'min_samples_leaf': 6, 'min_samples_split': 176},
mean: 0.80875, std: 0.00356, params: {'min_samples_leaf': 1, 'min_samples_split': 199},
mean: 0.80875, std: 0.00387, params: {'min_samples_leaf': 5, 'min_samples_split': 67},
mean: 0.80880, std: 0.00124, params: {'min_samples_leaf': 6, 'min_samples_split': 71},
mean: 0.80880, std: 0.00440, params: {'min_samples_leaf': 6, 'min_samples_split': 180},
mean: 0.80880, std: 0.00239, params: {'min_samples_leaf': 5, 'min_samples_split': 78},
mean: 0.80895, std: 0.00039, params: {'min_samples_leaf': 4, 'min_samples_split': 42},
mean: 0.80895, std: 0.00284, params: {'min_samples_leaf': 7, 'min_samples_split': 102},
mean: 0.80905, std: 0.00531, params: {'min_samples_leaf': 6, 'min_samples_split': 45},
mean: 0.80905, std: 0.00251, params: {'min_samples_leaf': 6, 'min_samples_split': 187},
mean: 0.80910, std: 0.00136, params: {'min_samples_leaf': 7, 'min_samples_split': 174},
mean: 0.80915, std: 0.00287, params: {'min_samples_leaf': 4, 'min_samples_split': 72},
mean: 0.80915, std: 0.00254, params: {'min_samples_leaf': 4, 'min_samples_split': 107},
mean: 0.80915, std: 0.00360, params: {'min_samples_leaf': 4, 'min_samples_split': 27},
mean: 0.80920, std: 0.00235, params: {'min_samples_leaf': 2, 'min_samples_split': 117},
mean: 0.80920, std: 0.00098, params: {'min_samples_leaf': 7, 'min_samples_split': 104},
mean: 0.80920, std: 0.00044, params: {'min_samples_leaf': 6, 'min_samples_split': 132},
mean: 0.80920, std: 0.00170, params: {'min_samples_leaf': 6, 'min_samples_split': 133},
mean: 0.80925, std: 0.00381, params: {'min_samples_leaf': 3, 'min_samples_split': 189},
mean: 0.80925, std: 0.00271, params: {'min_samples_leaf': 5, 'min_samples_split': 137},
mean: 0.80930, std: 0.00377, params: {'min_samples_leaf': 4, 'min_samples_split': 152},
mean: 0.80935, std: 0.00470, params: {'min_samples_leaf': 6, 'min_samples_split': 36},
mean: 0.80935, std: 0.00234, params: {'min_samples_leaf': 4, 'min_samples_split': 133},
mean: 0.80940, std: 0.00348, params: {'min_samples_leaf': 3, 'min_samples_split': 14},
mean: 0.80945, std: 0.00173, params: {'min_samples_leaf': 1, 'min_samples_split': 101},
mean: 0.80950, std: 0.00182, params: {'min_samples_leaf': 8, 'min_samples_split': 34},
mean: 0.80950, std: 0.00495, params: {'min_samples_leaf': 5, 'min_samples_split': 166},
mean: 0.80960, std: 0.00220, params: {'min_samples_leaf': 2, 'min_samples_split': 184},
mean: 0.80960, std: 0.00327, params: {'min_samples_leaf': 1, 'min_samples_split': 178},
mean: 0.80965, std: 0.00066, params: {'min_samples_leaf': 2, 'min_samples_split': 127},
mean: 0.80970, std: 0.00188, params: {'min_samples_leaf': 4, 'min_samples_split': 164},
mean: 0.80970, std: 0.00137, params: {'min_samples_leaf': 7, 'min_samples_split': 24},
mean: 0.80975, std: 0.00300, params: {'min_samples_leaf': 2, 'min_samples_split': 13},
mean: 0.80980, std: 0.00116, params: {'min_samples_leaf': 1, 'min_samples_split': 163},
mean: 0.80980, std: 0.00057, params: {'min_samples_leaf': 2, 'min_samples_split': 75},
mean: 0.80980, std: 0.00288, params: {'min_samples_leaf': 2, 'min_samples_split': 189},
mean: 0.80985, std: 0.00218, params: {'min_samples_leaf': 1, 'min_samples_split': 55},
mean: 0.80985, std: 0.00069, params: {'min_samples_leaf': 6, 'min_samples_split': 47},
mean: 0.80985, std: 0.00167, params: {'min_samples_leaf': 4, 'min_samples_split': 165},
mean: 0.80985, std: 0.00061, params: {'min_samples_leaf': 4, 'min_samples_split': 162},
mean: 0.80995, std: 0.00226, params: {'min_samples_leaf': 3, 'min_samples_split': 168},
mean: 0.80995, std: 0.00077, params: {'min_samples_leaf': 1, 'min_samples_split': 100},
mean: 0.81000, std: 0.00169, params: {'min_samples_leaf': 2, 'min_samples_split': 133},
mean: 0.81010, std: 0.00256, params: {'min_samples_leaf': 3, 'min_samples_split': 182},
mean: 0.81010, std: 0.00330, params: {'min_samples_leaf': 2, 'min_samples_split': 92},
mean: 0.81015, std: 0.00129, params: {'min_samples_leaf': 4, 'min_samples_split': 147},
mean: 0.81015, std: 0.00225, params: {'min_samples_leaf': 7, 'min_samples_split': 176},
mean: 0.81020, std: 0.00236, params: {'min_samples_leaf': 1, 'min_samples_split': 145},
mean: 0.81030, std: 0.00270, params: {'min_samples_leaf': 8, 'min_samples_split': 100},
mean: 0.81035, std: 0.00269, params: {'min_samples_leaf': 4, 'min_samples_split': 174},
mean: 0.81035, std: 0.00214, params: {'min_samples_leaf': 2, 'min_samples_split': 191},
mean: 0.81035, std: 0.00160, params: {'min_samples_leaf': 1, 'min_samples_split': 95},
mean: 0.81040, std: 0.00127, params: {'min_samples_leaf': 6, 'min_samples_split': 49},
mean: 0.81040, std: 0.00522, params: {'min_samples_leaf': 5, 'min_samples_split': 124},
mean: 0.81040, std: 0.00099, params: {'min_samples_leaf': 1, 'min_samples_split': 112},
mean: 0.81045, std: 0.00153, params: {'min_samples_leaf': 4, 'min_samples_split': 106},
mean: 0.81050, std: 0.00306, params: {'min_samples_leaf': 1, 'min_samples_split': 151},
mean: 0.81050, std: 0.00332, params: {'min_samples_leaf': 2, 'min_samples_split': 71},
mean: 0.81055, std: 0.00289, params: {'min_samples_leaf': 3, 'min_samples_split': 154},
mean: 0.81055, std: 0.00279, params: {'min_samples_leaf': 3, 'min_samples_split': 183},
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