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Output Analysis
Abu Zaher Md. Faridee edited this page Aug 12, 2012
·
31 revisions
- Macbook 2007 C2D Python
numDecisionTrees = 100
treeSplitCriterion='gainRatio'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 0.3
optimumFeatureSubsetSelectionCriteria = 'log2'
calcForrestErrorRate()
len(self.globalOutOfBagEstimates): 187
numCorrect 151
forrestErrorRate: 0.192513368984
calcForrestVariableImportance()
globalVariableRanks: [[9, 4.7], [2, 0.32], [264, 0.14], [11, 0.12], [175, 0.12], [41, 0.11], [61, 0.11], [44, 0.1], [1, 0.09], [70, 0.09], [112, 0.08], [302, 0.08], [14, 0.07], [24, 0.07], [31, 0.07], [43, 0.07], [64, 0.07], [86, 0.07], [144, 0.07], [568, 0.07], [22, 0.06], [33, 0.06], [88, 0.06], [119, 0.06], [145, 0.06], [159, 0.06], [390, 0.06], [15, 0.05], [16, 0.05], [32, 0.05], [40, 0.05], [57, 0.05], [76, 0.05], [77, 0.05], [129, 0.05], [141, 0.05], [151, 0.05], [161, 0.05], [254, 0.05], [4, 0.04], [21, 0.04], [25, 0.04], [26, 0.04], [27, 0.04], [34, 0.04], [47, 0.04], [49, 0.04], [58, 0.04], [69, 0.04], [108, 0.04], [114, 0.04], [125, 0.04], [158, 0.04], [184, 0.04], [258, 0.04], [286, 0.04], [562, 0.04], [37, 0.03], [48, 0.03], [62, 0.03], [68, 0.03], [78, 0.03], [84, 0.03], [91, 0.03], [100, 0.03], [133, 0.03], [157, 0.03], [160, 0.03], [182, 0.03], [195, 0.03], [256, 0.03], [285, 0.03], [514, 0.03], [7, 0.02], [29, 0.02], [35, 0.02], [39, 0.02], [42, 0.02], [51, 0.02], [93, 0.02], [105, 0.02], [116, 0.02], [127, 0.02], [128, 0.02], [139, 0.02], [140, 0.02], [156, 0.02], [176, 0.02], [196, 0.02], [224, 0.02], [228, 0.02], [231, 0.02], [262, 0.02], [313, 0.02], [319, 0.02], [418, 0.02], [421, 0.02], [468, 0.02], [523, 0.02], [532, 0.02], [572, 0.02], [0, 0.01], [28, 0.01], [30, 0.01], [36, 0.01], [46, 0.01], [52, 0.01], [56, 0.01], [67, 0.01], [71, 0.01], [72, 0.01], [73, 0.01], [74, 0.01], [80, 0.01], [85, 0.01], [98, 0.01], [118, 0.01], [122, 0.01], [154, 0.01], [155, 0.01], [183, 0.01], [204, 0.01], [212, 0.01], [230, 0.01], [232, 0.01], [233, 0.01], [234, 0.01], [240, 0.01], [261, 0.01], [267, 0.01], [272, 0.01], [280, 0.01], [293, 0.01], [371, 0.01], [379, 0.01], [425, 0.01], [432, 0.01], [435, 0.01], [461, 0.01], [483, 0.01], [494, 0.01], [630, 0.01], [633, 0.01], [638, 0.01], [682, 0.01], [737, 0.01]]
real 10m36.912s
user 9m35.138s
sys 0m41.966s
Macbook 2007 C2D Python
numDecisionTrees = 1000
treeSplitCriterion='gainRatio'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 0.3
optimumFeatureSubsetSelectionCriteria = 'log2'
calcForrestErrorRate()
len(self.globalOutOfBagEstimates): 187
numCorrect 152
forrestErrorRate: 0.187165775401
calcForrestVariableImportance()
globalVariableRanks: [[9, 4.581], [2, 0.357], [14, 0.112], [41, 0.102], [43, 0.102], [33, 0.099], [264, 0.099], [31, 0.083], [34, 0.081], [182, 0.074], [11, 0.073], [64, 0.072], [27, 0.062], [100, 0.062], [39, 0.058], [16, 0.056], [112, 0.055], [86, 0.054], [36, 0.052], [145, 0.049], [161, 0.049], [22, 0.048], [88, 0.048], [133, 0.048], [140, 0.048], [7, 0.045], [129, 0.045], [24, 0.044], [47, 0.042], [141, 0.042], [302, 0.041], [77, 0.04], [144, 0.038], [117, 0.037], [26, 0.036], [32, 0.036], [228, 0.036], [91, 0.034], [105, 0.034], [114, 0.034], [57, 0.033], [158, 0.032], [25, 0.031], [37, 0.031], [68, 0.031], [58, 0.029], [10, 0.027], [44, 0.027], [1, 0.026], [12, 0.026], [23, 0.026], [28, 0.024], [40, 0.024], [71, 0.024], [42, 0.023], [51, 0.023], [159, 0.023], [360, 0.023], [60, 0.022], [72, 0.022], [390, 0.022], [70, 0.021], [98, 0.021], [84, 0.02], [21, 0.019], [127, 0.019], [73, 0.018], [285, 0.018], [157, 0.017], [195, 0.017], [65, 0.016], [89, 0.016], [119, 0.016], [125, 0.016], [286, 0.016], [365, 0.015], [371, 0.015], [3, 0.014], [69, 0.014], [79, 0.014], [35, 0.013], [82, 0.013], [176, 0.013], [230, 0.013], [254, 0.013], [256, 0.013], [30, 0.012], [49, 0.012], [52, 0.012], [78, 0.012], [234, 0.012], [568, 0.012], [55, 0.011], [61, 0.011], [151, 0.011], [174, 0.011], [296, 0.011], [406, 0.011], [468, 0.011], [87, 0.01], [148, 0.01], [232, 0.01], [273, 0.01], [407, 0.01], [48, 0.009], [74, 0.009], [93, 0.009], [204, 0.009], [277, 0.009], [414, 0.009], [418, 0.009], [456, 0.009], [13, 0.008], [56, 0.008], [259, 0.008], [314, 0.008], [379, 0.008], [532, 0.008], [677, 0.008], [17, 0.007], [85, 0.007], [102, 0.007], [116, 0.007], [212, 0.007], [373, 0.007], [421, 0.007], [427, 0.007], [29, 0.006], [104, 0.006], [142, 0.006], [190, 0.006], [243, 0.006], [280, 0.006], [304, 0.006], [308, 0.006], [334, 0.006], [338, 0.006], [346, 0.006], [673, 0.006], [80, 0.005], [92, 0.005], [128, 0.005], [143, 0.005], [180, 0.005], [184, 0.005], [187, 0.005], [219, 0.005], [240, 0.005], [313, 0.005], [389, 0.005], [399, 0.005], [458, 0.005], [554, 0.005], [682, 0.005], [90, 0.004], [118, 0.004], [123, 0.004], [154, 0.004], [258, 0.004], [276, 0.004], [426, 0.004], [432, 0.004], [435, 0.004], [523, 0.004], [572, 0.004], [635, 0.004], [643, 0.004], [15, 0.003], [46, 0.003], [75, 0.003], [76, 0.003], [101, 0.003], [107, 0.003], [120, 0.003], [178, 0.003], [215, 0.003], [224, 0.003], [255, 0.003], [347, 0.003], [400, 0.003], [405, 0.003], [420, 0.003], [497, 0.003], [582, 0.003], [633, 0.003], [54, 0.002], [97, 0.002], [99, 0.002], [109, 0.002], [196, 0.002], [211, 0.002], [220, 0.002], [252, 0.002], [261, 0.002], [297, 0.002], [322, 0.002], [357, 0.002], [382, 0.002], [416, 0.002], [483, 0.002], [615, 0.002], [619, 0.002], [636, 0.002], [642, 0.002], [728, 0.002], [785, 0.002], [126, 0.001], [152, 0.001], [155, 0.001], [163, 0.001], [168, 0.001], [188, 0.001], [191, 0.001], [200, 0.001], [205, 0.001], [213, 0.001], [217, 0.001], [260, 0.001], [267, 0.001], [278, 0.001], [287, 0.001], [295, 0.001], [315, 0.001], [320, 0.001], [327, 0.001], [340, 0.001], [367, 0.001], [369, 0.001], [381, 0.001], [412, 0.001], [467, 0.001], [514, 0.001], [562, 0.001], [586, 0.001], [608, 0.001], [618, 0.001], [623, 0.001], [638, 0.001], [639, 0.001], [737, 0.001]]
real 103m34.918s
user 98m28.759s
sys 4m38.104s
Macbook 2007 C2D Python
numDecisionTrees = 1000
treeSplitCriterion='gainRatio'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 0.3
optimumFeatureSubsetSelectionCriteria = 'log2'
calcForrestErrorRate()
len(self.globalOutOfBagEstimates): 187
numCorrect 149
forrestErrorRate: 0.20320855615
calcForrestVariableImportance()
globalVariableRanks: [[9, 4.667], [2, 0.377], [264, 0.12], [31, 0.11], [7, 0.093], [33, 0.09], [182, 0.076], [11, 0.074], [100, 0.07], [14, 0.069], [39, 0.065], [16, 0.064], [41, 0.064], [145, 0.064], [27, 0.061], [158, 0.06], [151, 0.058], [34, 0.056], [43, 0.055], [60, 0.053], [86, 0.052], [112, 0.051], [24, 0.05], [133, 0.05], [88, 0.049], [161, 0.049], [141, 0.048], [47, 0.046], [12, 0.045], [140, 0.042], [23, 0.04], [117, 0.038], [3, 0.035], [68, 0.034], [32, 0.033], [228, 0.033], [21, 0.032], [105, 0.032], [36, 0.031], [144, 0.031], [1, 0.03], [26, 0.03], [35, 0.03], [57, 0.03], [91, 0.03], [71, 0.029], [296, 0.028], [84, 0.027], [157, 0.027], [65, 0.024], [72, 0.024], [22, 0.023], [61, 0.023], [82, 0.023], [77, 0.022], [58, 0.021], [302, 0.021], [40, 0.02], [114, 0.02], [159, 0.02], [51, 0.019], [73, 0.019], [127, 0.019], [129, 0.019], [371, 0.019], [87, 0.018], [37, 0.017], [49, 0.017], [64, 0.017], [70, 0.017], [78, 0.017], [25, 0.016], [286, 0.016], [407, 0.016], [98, 0.015], [119, 0.015], [156, 0.015], [285, 0.015], [421, 0.015], [184, 0.014], [154, 0.013], [195, 0.013], [10, 0.012], [92, 0.012], [204, 0.012], [390, 0.012], [399, 0.012], [79, 0.011], [187, 0.011], [232, 0.011], [255, 0.011], [568, 0.011], [50, 0.01], [258, 0.01], [331, 0.01], [636, 0.01], [143, 0.009], [234, 0.009], [277, 0.009], [52, 0.008], [74, 0.008], [90, 0.008], [230, 0.008], [280, 0.008], [360, 0.008], [414, 0.008], [418, 0.008], [42, 0.007], [108, 0.007], [160, 0.007], [259, 0.007], [262, 0.007], [273, 0.007], [46, 0.006], [85, 0.006], [89, 0.006], [125, 0.006], [190, 0.006], [202, 0.006], [219, 0.006], [243, 0.006], [267, 0.006], [468, 0.006], [572, 0.006], [28, 0.005], [102, 0.005], [116, 0.005], [118, 0.005], [178, 0.005], [220, 0.005], [268, 0.005], [293, 0.005], [313, 0.005], [346, 0.005], [405, 0.005], [29, 0.004], [99, 0.004], [104, 0.004], [155, 0.004], [256, 0.004], [338, 0.004], [400, 0.004], [406, 0.004], [472, 0.004], [554, 0.004], [623, 0.004], [638, 0.004], [702, 0.004], [4, 0.003], [17, 0.003], [69, 0.003], [76, 0.003], [81, 0.003], [122, 0.003], [169, 0.003], [212, 0.003], [231, 0.003], [240, 0.003], [308, 0.003], [339, 0.003], [362, 0.003], [367, 0.003], [373, 0.003], [435, 0.003], [458, 0.003], [494, 0.003], [562, 0.003], [673, 0.003], [13, 0.002], [54, 0.002], [56, 0.002], [63, 0.002], [80, 0.002], [120, 0.002], [123, 0.002], [128, 0.002], [139, 0.002], [176, 0.002], [254, 0.002], [261, 0.002], [281, 0.002], [314, 0.002], [377, 0.002], [420, 0.002], [429, 0.002], [514, 0.002], [615, 0.002], [677, 0.002], [682, 0.002], [15, 0.001], [62, 0.001], [83, 0.001], [93, 0.001], [101, 0.001], [106, 0.001], [107, 0.001], [142, 0.001], [183, 0.001], [188, 0.001], [191, 0.001], [200, 0.001], [218, 0.001], [276, 0.001], [278, 0.001], [334, 0.001], [336, 0.001], [347, 0.001], [348, 0.001], [355, 0.001], [357, 0.001], [379, 0.001], [389, 0.001], [409, 0.001], [412, 0.001], [413, 0.001], [456, 0.001], [598, 0.001], [642, 0.001], [643, 0.001], [679, 0.001], [737, 0.001], [785, 0.001]]
real 100m23.321s
user 94m39.627s
sys 5m14.435s
Some previous runs can be found here:
Macbook 2007 C2D Python
numDecisionTrees = 100
treeSplitCriterion='gainRatio'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 1
optimumFeatureSubsetSelectionCriteria = 'log2'
numCorrect 41
forrestErrorRate: 0.539325842697
globalVariableRanks: [[9, 0.35], [286, 0.1], [14, 0.08], [413, 0.08], [552, 0.07], [8, 0.06], [10, 0.06], [12, 0.06], [126, 0.06], [463, 0.06], [532, 0.06], [951, 0.06], [15, 0.05], [45, 0.05], [89, 0.05], [63, 0.04], [203, 0.04], [207, 0.04], [239, 0.04], [401, 0.04], [465, 0.04], [1002, 0.04], [1088, 0.04], [1092, 0.04], [4, 0.03], [29, 0.03], [33, 0.03], [37, 0.03], [86, 0.03], [102, 0.03], [172, 0.03], [202, 0.03], [224, 0.03], [226, 0.03], [229, 0.03], [280, 0.03], [430, 0.03], [461, 0.03], [791, 0.03], [967, 0.03], [1102, 0.03], [1, 0.02], [2, 0.02], [19, 0.02], [39, 0.02], [40, 0.02], [41, 0.02], [55, 0.02], [59, 0.02], [71, 0.02], [77, 0.02], [80, 0.02], [100, 0.02], [182, 0.02], [188, 0.02], [189, 0.02], [215, 0.02], [258, 0.02], [295, 0.02], [302, 0.02], [339, 0.02], [353, 0.02], [358, 0.02], [506, 0.02], [547, 0.02], [609, 0.02], [755, 0.02], [760, 0.02], [1867, 0.02], [0, 0.01], [7, 0.01], [17, 0.01], [18, 0.01], [21, 0.01], [22, 0.01], [24, 0.01], [32, 0.01], [49, 0.01], [50, 0.01], [61, 0.01], [62, 0.01], [66, 0.01], [67, 0.01], [70, 0.01], [75, 0.01], [84, 0.01], [94, 0.01], [107, 0.01], [110, 0.01], [117, 0.01], [120, 0.01], [124, 0.01], [125, 0.01], [131, 0.01], [135, 0.01], [143, 0.01], [146, 0.01], [160, 0.01], [167, 0.01], [168, 0.01], [170, 0.01], [176, 0.01], [197, 0.01], [205, 0.01], [212, 0.01], [216, 0.01], [220, 0.01], [221, 0.01], [234, 0.01], [241, 0.01], [243, 0.01], [247, 0.01], [250, 0.01], [254, 0.01], [264, 0.01], [269, 0.01], [270, 0.01], [276, 0.01], [278, 0.01], [284, 0.01], [290, 0.01], [292, 0.01], [294, 0.01], [300, 0.01], [306, 0.01], [320, 0.01], [352, 0.01], [368, 0.01], [377, 0.01], [382, 0.01], [388, 0.01], [408, 0.01], [447, 0.01], [458, 0.01], [478, 0.01], [479, 0.01], [480, 0.01], [493, 0.01], [527, 0.01], [530, 0.01], [533, 0.01], [537, 0.01], [572, 0.01], [578, 0.01], [610, 0.01], [615, 0.01], [616, 0.01], [631, 0.01], [664, 0.01], [673, 0.01], [677, 0.01], [707, 0.01], [745, 0.01], [780, 0.01], [799, 0.01], [822, 0.01], [828, 0.01], [838, 0.01], [859, 0.01], [867, 0.01], [881, 0.01], [892, 0.01], [906, 0.01], [979, 0.01], [1019, 0.01], [1024, 0.01], [1045, 0.01], [1053, 0.01], [1071, 0.01], [1104, 0.01], [1151, 0.01], [1225, 0.01], [1276, 0.01], [1363, 0.01], [1493, 0.01], [1618, 0.01], [1666, 0.01], [1667, 0.01], [1681, 0.01], [1700, 0.01], [1796, 0.01], [1886, 0.01], [2059, 0.01], [2387, 0.01]]
real 33m32.336s
user 31m54.014s
sys 1m33.298s
Macbook 2007 C2D Python
numDecisionTrees = 100
treeSplitCriterion='gainRatio'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 1
optimumFeatureSubsetSelectionCriteria = 'log2'
numCorrect 36
forrestErrorRate: 0.595505617978
globalVariableRanks: [[9, 0.21], [413, 0.1], [14, 0.08], [269, 0.08], [463, 0.08], [4, 0.07], [203, 0.07], [244, 0.07], [286, 0.07], [3, 0.06], [76, 0.06], [77, 0.06], [90, 0.06], [465, 0.06], [15, 0.05], [18, 0.05], [22, 0.05], [160, 0.05], [229, 0.05], [405, 0.05], [552, 0.05], [717, 0.05], [24, 0.04], [149, 0.04], [202, 0.04], [212, 0.04], [462, 0.04], [530, 0.04], [531, 0.04], [612, 0.04], [27, 0.03], [30, 0.03], [31, 0.03], [32, 0.03], [40, 0.03], [62, 0.03], [74, 0.03], [81, 0.03], [97, 0.03], [99, 0.03], [151, 0.03], [167, 0.03], [361, 0.03], [382, 0.03], [558, 0.03], [578, 0.03], [616, 0.03], [684, 0.03], [718, 0.03], [1281, 0.03], [1363, 0.03], [0, 0.02], [2, 0.02], [7, 0.02], [8, 0.02], [10, 0.02], [25, 0.02], [34, 0.02], [46, 0.02], [57, 0.02], [60, 0.02], [64, 0.02], [65, 0.02], [82, 0.02], [100, 0.02], [126, 0.02], [148, 0.02], [180, 0.02], [187, 0.02], [207, 0.02], [252, 0.02], [270, 0.02], [295, 0.02], [298, 0.02], [300, 0.02], [346, 0.02], [352, 0.02], [387, 0.02], [401, 0.02], [420, 0.02], [422, 0.02], [445, 0.02], [473, 0.02], [570, 0.02], [677, 0.02], [712, 0.02], [780, 0.02], [816, 0.02], [866, 0.02], [1053, 0.02], [1088, 0.02], [2190, 0.02], [2404, 0.02], [2422, 0.02], [13, 0.01], [21, 0.01], [29, 0.01], [51, 0.01], [52, 0.01], [56, 0.01], [58, 0.01], [67, 0.01], [68, 0.01], [72, 0.01], [88, 0.01], [93, 0.01], [102, 0.01], [113, 0.01], [115, 0.01], [116, 0.01], [121, 0.01], [132, 0.01], [133, 0.01], [134, 0.01], [144, 0.01], [145, 0.01], [146, 0.01], [147, 0.01], [152, 0.01], [154, 0.01], [161, 0.01], [182, 0.01], [185, 0.01], [222, 0.01], [226, 0.01], [235, 0.01], [242, 0.01], [249, 0.01], [254, 0.01], [258, 0.01], [271, 0.01], [276, 0.01], [299, 0.01], [302, 0.01], [308, 0.01], [313, 0.01], [318, 0.01], [319, 0.01], [329, 0.01], [333, 0.01], [342, 0.01], [355, 0.01], [367, 0.01], [434, 0.01], [479, 0.01], [500, 0.01], [504, 0.01], [520, 0.01], [541, 0.01], [545, 0.01], [557, 0.01], [601, 0.01], [621, 0.01], [622, 0.01], [695, 0.01], [702, 0.01], [760, 0.01], [787, 0.01], [791, 0.01], [801, 0.01], [822, 0.01], [830, 0.01], [853, 0.01], [881, 0.01], [906, 0.01], [937, 0.01], [951, 0.01], [967, 0.01], [1045, 0.01], [1077, 0.01], [1101, 0.01], [1102, 0.01], [1163, 0.01], [1211, 0.01], [1319, 0.01], [1337, 0.01], [1381, 0.01], [1540, 0.01], [1575, 0.01], [1593, 0.01], [1634, 0.01], [1666, 0.01], [1710, 0.01], [1757, 0.01], [1827, 0.01], [1842, 0.01], [1846, 0.01], [2179, 0.01]]
real 34m17.674s
user 32m49.228s
sys 1m21.970s
Macbook 2007 C2D Python
numDecisionTrees = 100
treeSplitCriterion='informationGain'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 1
optimumFeatureSubsetSelectionCriteria = 'log2'
numCorrect 58
forrestErrorRate: 0.348314606742
globalVariableRanks: [[9, 0.22], [64, 0.22], [39, 0.15], [102, 0.1], [6, 0.09], [90, 0.09], [152, 0.09], [158, 0.09], [179, 0.09], [17, 0.08], [28, 0.08], [48, 0.08], [47, 0.07], [497, 0.07], [3, 0.06], [126, 0.06], [15, 0.05], [43, 0.05], [65, 0.05], [69, 0.05], [89, 0.05], [187, 0.05], [241, 0.05], [14, 0.04], [16, 0.04], [19, 0.04], [59, 0.04], [94, 0.04], [111, 0.04], [382, 0.04], [0, 0.03], [38, 0.03], [53, 0.03], [62, 0.03], [77, 0.03], [136, 0.03], [172, 0.03], [203, 0.03], [251, 0.03], [295, 0.03], [304, 0.03], [2, 0.02], [7, 0.02], [11, 0.02], [21, 0.02], [30, 0.02], [41, 0.02], [49, 0.02], [52, 0.02], [57, 0.02], [58, 0.02], [70, 0.02], [88, 0.02], [98, 0.02], [105, 0.02], [110, 0.02], [125, 0.02], [132, 0.02], [146, 0.02], [153, 0.02], [201, 0.02], [214, 0.02], [216, 0.02], [221, 0.02], [231, 0.02], [269, 0.02], [290, 0.02], [298, 0.02], [319, 0.02], [355, 0.02], [405, 0.02], [472, 0.02], [504, 0.02], [532, 0.02], [549, 0.02], [575, 0.02], [720, 0.02], [1002, 0.02], [10, 0.01], [12, 0.01], [18, 0.01], [23, 0.01], [27, 0.01], [29, 0.01], [31, 0.01], [33, 0.01], [34, 0.01], [42, 0.01], [44, 0.01], [66, 0.01], [71, 0.01], [75, 0.01], [85, 0.01], [87, 0.01], [93, 0.01], [96, 0.01], [108, 0.01], [113, 0.01], [116, 0.01], [119, 0.01], [121, 0.01], [128, 0.01], [144, 0.01], [149, 0.01], [168, 0.01], [180, 0.01], [185, 0.01], [193, 0.01], [202, 0.01], [205, 0.01], [215, 0.01], [220, 0.01], [237, 0.01], [243, 0.01], [250, 0.01], [254, 0.01], [258, 0.01], [264, 0.01], [265, 0.01], [280, 0.01], [300, 0.01], [306, 0.01], [324, 0.01], [331, 0.01], [354, 0.01], [375, 0.01], [384, 0.01], [470, 0.01], [473, 0.01], [479, 0.01], [489, 0.01], [569, 0.01], [631, 0.01], [634, 0.01], [677, 0.01], [707, 0.01], [745, 0.01], [822, 0.01]]
Macbook 2007 C2D Python
numDecisionTrees = 100
treeSplitCriterion='informationGain'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 1
optimumFeatureSubsetSelectionCriteria = 'log2'
numCorrect 50
forrestErrorRate: 0.438202247191
globalVariableRanks: [[90, 0.2], [9, 0.17], [152, 0.15], [89, 0.12], [465, 0.08], [5, 0.07], [12, 0.07], [14, 0.07], [72, 0.07], [75, 0.07], [121, 0.07], [125, 0.07], [8, 0.06], [28, 0.06], [30, 0.06], [40, 0.06], [102, 0.06], [6, 0.05], [20, 0.05], [23, 0.05], [53, 0.05], [87, 0.05], [158, 0.05], [203, 0.05], [231, 0.05], [286, 0.05], [481, 0.05], [2, 0.04], [15, 0.04], [17, 0.04], [18, 0.04], [19, 0.04], [68, 0.04], [88, 0.04], [269, 0.04], [334, 0.04], [1002, 0.04], [1, 0.03], [3, 0.03], [10, 0.03], [11, 0.03], [21, 0.03], [52, 0.03], [59, 0.03], [60, 0.03], [62, 0.03], [97, 0.03], [113, 0.03], [150, 0.03], [154, 0.03], [176, 0.03], [185, 0.03], [198, 0.03], [212, 0.03], [215, 0.03], [220, 0.03], [251, 0.03], [304, 0.03], [376, 0.03], [382, 0.03], [4, 0.02], [24, 0.02], [29, 0.02], [32, 0.02], [43, 0.02], [44, 0.02], [64, 0.02], [69, 0.02], [71, 0.02], [83, 0.02], [85, 0.02], [108, 0.02], [148, 0.02], [171, 0.02], [195, 0.02], [200, 0.02], [202, 0.02], [204, 0.02], [221, 0.02], [254, 0.02], [272, 0.02], [280, 0.02], [319, 0.02], [387, 0.02], [497, 0.02], [506, 0.02], [569, 0.02], [1053, 0.02], [7, 0.01], [16, 0.01], [25, 0.01], [27, 0.01], [38, 0.01], [41, 0.01], [47, 0.01], [65, 0.01], [70, 0.01], [74, 0.01], [81, 0.01], [82, 0.01], [86, 0.01], [98, 0.01], [104, 0.01], [110, 0.01], [112, 0.01], [122, 0.01], [124, 0.01], [126, 0.01], [130, 0.01], [134, 0.01], [145, 0.01], [147, 0.01], [149, 0.01], [179, 0.01], [180, 0.01], [188, 0.01], [189, 0.01], [193, 0.01], [197, 0.01], [217, 0.01], [235, 0.01], [244, 0.01], [260, 0.01], [261, 0.01], [274, 0.01], [294, 0.01], [328, 0.01], [344, 0.01], [375, 0.01], [401, 0.01], [407, 0.01], [413, 0.01], [428, 0.01], [430, 0.01], [436, 0.01], [462, 0.01], [463, 0.01], [478, 0.01], [479, 0.01], [487, 0.01], [514, 0.01], [529, 0.01], [532, 0.01], [552, 0.01], [565, 0.01], [696, 0.01], [707, 0.01], [781, 0.01], [830, 0.01], [859, 0.01], [937, 0.01], [1616, 0.01], [2046, 0.01]]
real 30m45.743s
user 29m24.622s
sys 1m15.719s
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informationGainseems be giving better accuracy thangainRatiofor this dataset