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Output Analysis
Abu Zaher Md. Faridee edited this page Aug 12, 2012
·
31 revisions
numSamples: 187
numFeatures: 845
- 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
numSamplesPerFeatures: 0.221301775148
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
numSamplesPerFeatures: 0.221301775148
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
numSamplesPerFeatures: 0.221301775148
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:
numSamples: 341
numFeatures: 1847
numSamplesPerFeatures: 0.184623714131
numSamples: 89
numFeatures: 2538
Macbook 2007 C2D Python
numDecisionTrees = 100
treeSplitCriterion='gainRatio'
doPruning = True
pruneAggressiveness = 0.9
discardHighErrorTrees = True
highErrorTreeDiscardThreshold = 1
optimumFeatureSubsetSelectionCriteria = 'log2'
numCorrect 41
numSamplesPerFeatures: 0.0350669818755
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
numSamplesPerFeatures: 0.0350669818755
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
numSamplesPerFeatures: 0.0350669818755
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
numSamplesPerFeatures: 0.0350669818755
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
Macbook Pro 2010 C2D Python
numDecisionTrees = 1000,
treeSplitCriterion='informationGain',
pruneAggressiveness = 0.9,
highErrorTreeDiscardThreshold = 1,
optimumFeatureSubsetSelectionCriteria = 'log2',
featureStandardDeviationThreshold= 0.1
numCorrect 68
numSamplesPerFeatures: 0.0350669818755
forrestErrorRate: 0.23595505618
globalVariableRanks: [[9, 0.187], [14, 0.105], [6, 0.095], [90, 0.085], [152, 0.083], [75, 0.076], [102, 0.076], [63, 0.074], [0, 0.068], [64, 0.062], [77, 0.06], [15, 0.056], [8, 0.055], [12, 0.054], [27, 0.049], [1, 0.045], [5, 0.044], [3, 0.043], [18, 0.043], [179, 0.043], [105, 0.042], [552, 0.042], [62, 0.041], [465, 0.041], [215, 0.04], [30, 0.039], [251, 0.039], [39, 0.038], [32, 0.037], [23, 0.036], [29, 0.036], [59, 0.035], [33, 0.033], [88, 0.033], [205, 0.033], [158, 0.032], [24, 0.031], [126, 0.031], [7, 0.03], [48, 0.03], [28, 0.029], [99, 0.029], [286, 0.029], [72, 0.028], [176, 0.028], [202, 0.028], [413, 0.028], [13, 0.027], [65, 0.027], [16, 0.026], [17, 0.026], [38, 0.026], [41, 0.026], [87, 0.026], [269, 0.026], [22, 0.025], [89, 0.025], [128, 0.025], [19, 0.024], [51, 0.023], [71, 0.023], [110, 0.022], [319, 0.022], [108, 0.021], [160, 0.021], [476, 0.021], [2, 0.02], [21, 0.02], [214, 0.02], [130, 0.019], [382, 0.019], [4, 0.018], [295, 0.018], [43, 0.017], [121, 0.017], [463, 0.017], [34, 0.016], [54, 0.016], [85, 0.016], [93, 0.016], [47, 0.015], [69, 0.015], [144, 0.015], [145, 0.015], [168, 0.015], [203, 0.015], [212, 0.015], [244, 0.015], [57, 0.014], [337, 0.014], [58, 0.013], [112, 0.013], [174, 0.013], [280, 0.013], [497, 0.013], [512, 0.013], [20, 0.012], [49, 0.012], [167, 0.012], [355, 0.012], [55, 0.011], [61, 0.011], [278, 0.011], [711, 0.011], [52, 0.01], [125, 0.01], [139, 0.01], [153, 0.01], [201, 0.01], [216, 0.01], [506, 0.01], [514, 0.01], [25, 0.009], [50, 0.009], [117, 0.009], [221, 0.009], [241, 0.009], [270, 0.009], [387, 0.009], [10, 0.008], [81, 0.008], [190, 0.008], [254, 0.008], [258, 0.008], [276, 0.008], [396, 0.008], [531, 0.008], [569, 0.008], [575, 0.008], [859, 0.008], [40, 0.007], [94, 0.007], [149, 0.007], [185, 0.007], [229, 0.007], [293, 0.007], [460, 0.007], [533, 0.007], [631, 0.007], [707, 0.007], [1092, 0.007], [44, 0.006], [76, 0.006], [83, 0.006], [147, 0.006], [154, 0.006], [247, 0.006], [304, 0.006], [306, 0.006], [334, 0.006], [363, 0.006], [454, 0.006], [458, 0.006], [515, 0.006], [555, 0.006], [813, 0.006], [68, 0.005], [70, 0.005], [86, 0.005], [96, 0.005], [135, 0.005], [155, 0.005], [199, 0.005], [211, 0.005], [220, 0.005], [347, 0.005], [366, 0.005], [388, 0.005], [433, 0.005], [461, 0.005], [532, 0.005], [732, 0.005], [951, 0.005], [37, 0.004], [132, 0.004], [134, 0.004], [146, 0.004], [148, 0.004], [187, 0.004], [197, 0.004], [228, 0.004], [242, 0.004], [253, 0.004], [263, 0.004], [268, 0.004], [316, 0.004], [358, 0.004], [445, 0.004], [472, 0.004], [530, 0.004], [558, 0.004], [578, 0.004], [634, 0.004], [660, 0.004], [668, 0.004], [46, 0.003], [97, 0.003], [114, 0.003], [172, 0.003], [180, 0.003], [188, 0.003], [192, 0.003], [193, 0.003], [222, 0.003], [234, 0.003], [283, 0.003], [302, 0.003], [359, 0.003], [426, 0.003], [477, 0.003], [509, 0.003], [511, 0.003], [572, 0.003], [673, 0.003], [735, 0.003], [788, 0.003], [1002, 0.003], [1053, 0.003], [1102, 0.003], [1112, 0.003], [1710, 0.003], [74, 0.002], [104, 0.002], [138, 0.002], [207, 0.002], [217, 0.002], [231, 0.002], [235, 0.002], [237, 0.002], [239, 0.002], [265, 0.002], [281, 0.002], [288, 0.002], [324, 0.002], [328, 0.002], [342, 0.002], [354, 0.002], [361, 0.002], [367, 0.002], [380, 0.002], [401, 0.002], [405, 0.002], [428, 0.002], [432, 0.002], [447, 0.002], [457, 0.002], [473, 0.002], [478, 0.002], [479, 0.002], [489, 0.002], [499, 0.002], [508, 0.002], [602, 0.002], [612, 0.002], [676, 0.002], [717, 0.002], [720, 0.002], [747, 0.002], [751, 0.002], [755, 0.002], [762, 0.002], [810, 0.002], [853, 0.002], [870, 0.002], [984, 0.002], [1045, 0.002], [1088, 0.002], [1200, 0.002], [1421, 0.002], [1667, 0.002], [1693, 0.002], [26, 0.001], [36, 0.001], [67, 0.001], [82, 0.001], [106, 0.001], [120, 0.001], [129, 0.001], [131, 0.001], [140, 0.001], [141, 0.001], [182, 0.001], [195, 0.001], [196, 0.001], [226, 0.001], [227, 0.001], [230, 0.001], [243, 0.001], [255, 0.001], [264, 0.001], [271, 0.001], [284, 0.001], [325, 0.001], [331, 0.001], [352, 0.001], [356, 0.001], [360, 0.001], [368, 0.001], [386, 0.001], [389, 0.001], [394, 0.001], [395, 0.001], [400, 0.001], [412, 0.001], [434, 0.001], [438, 0.001], [444, 0.001], [500, 0.001], [516, 0.001], [526, 0.001], [541, 0.001], [545, 0.001], [546, 0.001], [554, 0.001], [582, 0.001], [589, 0.001], [591, 0.001], [594, 0.001], [605, 0.001], [607, 0.001], [616, 0.001], [622, 0.001], [677, 0.001], [691, 0.001], [692, 0.001], [696, 0.001], [724, 0.001], [729, 0.001], [761, 0.001], [780, 0.001], [791, 0.001], [800, 0.001], [830, 0.001], [838, 0.001], [867, 0.001], [892, 0.001], [897, 0.001], [903, 0.001], [921, 0.001], [952, 0.001], [985, 0.001], [1012, 0.001], [1033, 0.001], [1071, 0.001], [1074, 0.001], [1119, 0.001], [1129, 0.001], [1138, 0.001], [1146, 0.001], [1225, 0.001], [1262, 0.001], [1268, 0.001], [1740, 0.001], [1741, 0.001], [1796, 0.001], [1851, 0.001], [2335, 0.001], [2378, 0.001]]
real 309m8.566s
user 306m14.523s
sys 0m31.325s
Macbook 2007 C2D Python
numDecisionTrees = 1000,
treeSplitCriterion='informationGain',
pruneAggressiveness = 0.9,
highErrorTreeDiscardThreshold = 1,
optimumFeatureSubsetSelectionCriteria = 'log2',
featureStandardDeviationThreshold= 0.0
numCorrect 64
numSamplesPerFeatures: 0.0350669818755
forrestErrorRate: 0.280898876404
globalVariableRanks: [[9, 0.169], [14, 0.111], [64, 0.089], [102, 0.078], [12, 0.076], [8, 0.07], [77, 0.067], [27, 0.064], [63, 0.064], [32, 0.063], [90, 0.058], [23, 0.057], [286, 0.053], [39, 0.051], [6, 0.049], [48, 0.048], [152, 0.048], [126, 0.047], [203, 0.045], [5, 0.044], [18, 0.044], [465, 0.042], [552, 0.041], [0, 0.04], [3, 0.04], [28, 0.04], [40, 0.04], [244, 0.04], [251, 0.039], [215, 0.038], [205, 0.036], [269, 0.036], [413, 0.033], [22, 0.032], [75, 0.032], [2, 0.031], [24, 0.031], [179, 0.031], [11, 0.03], [65, 0.03], [130, 0.029], [59, 0.028], [202, 0.028], [30, 0.027], [10, 0.026], [29, 0.026], [158, 0.026], [89, 0.025], [105, 0.025], [214, 0.025], [41, 0.024], [86, 0.024], [88, 0.024], [295, 0.024], [51, 0.023], [53, 0.023], [72, 0.023], [94, 0.023], [231, 0.023], [20, 0.022], [99, 0.022], [174, 0.022], [239, 0.022], [1, 0.021], [13, 0.021], [15, 0.02], [19, 0.02], [47, 0.02], [85, 0.02], [93, 0.02], [117, 0.02], [69, 0.019], [144, 0.019], [176, 0.019], [382, 0.019], [4, 0.018], [16, 0.018], [62, 0.018], [108, 0.018], [146, 0.018], [25, 0.017], [128, 0.017], [17, 0.016], [276, 0.016], [859, 0.016], [7, 0.015], [70, 0.015], [76, 0.015], [161, 0.015], [188, 0.015], [254, 0.015], [304, 0.015], [34, 0.014], [38, 0.014], [43, 0.014], [58, 0.014], [168, 0.014], [229, 0.014], [21, 0.013], [160, 0.013], [334, 0.013], [799, 0.013], [124, 0.012], [185, 0.012], [135, 0.011], [136, 0.011], [258, 0.011], [463, 0.011], [575, 0.011], [87, 0.01], [337, 0.01], [216, 0.009], [221, 0.009], [268, 0.009], [319, 0.009], [478, 0.009], [506, 0.009], [511, 0.009], [54, 0.008], [55, 0.008], [57, 0.008], [68, 0.008], [172, 0.008], [187, 0.008], [272, 0.008], [359, 0.008], [405, 0.008], [622, 0.008], [676, 0.008], [36, 0.007], [46, 0.007], [110, 0.007], [112, 0.007], [132, 0.007], [147, 0.007], [270, 0.007], [353, 0.007], [711, 0.007], [33, 0.006], [52, 0.006], [81, 0.006], [113, 0.006], [154, 0.006], [167, 0.006], [171, 0.006], [217, 0.006], [242, 0.006], [355, 0.006], [392, 0.006], [479, 0.006], [487, 0.006], [497, 0.006], [512, 0.006], [1002, 0.006], [97, 0.005], [122, 0.005], [193, 0.005], [241, 0.005], [347, 0.005], [348, 0.005], [350, 0.005], [445, 0.005], [500, 0.005], [530, 0.005], [558, 0.005], [684, 0.005], [903, 0.005], [951, 0.005], [37, 0.004], [49, 0.004], [61, 0.004], [129, 0.004], [145, 0.004], [153, 0.004], [207, 0.004], [212, 0.004], [280, 0.004], [290, 0.004], [302, 0.004], [489, 0.004], [531, 0.004], [569, 0.004], [674, 0.004], [791, 0.004], [822, 0.004], [853, 0.004], [866, 0.004], [881, 0.004], [909, 0.004], [26, 0.003], [44, 0.003], [56, 0.003], [74, 0.003], [125, 0.003], [134, 0.003], [138, 0.003], [139, 0.003], [195, 0.003], [200, 0.003], [237, 0.003], [243, 0.003], [299, 0.003], [333, 0.003], [339, 0.003], [358, 0.003], [370, 0.003], [401, 0.003], [428, 0.003], [533, 0.003], [555, 0.003], [578, 0.003], [660, 0.003], [755, 0.003], [805, 0.003], [1102, 0.003], [1225, 0.003], [1458, 0.003], [35, 0.002], [45, 0.002], [71, 0.002], [83, 0.002], [142, 0.002], [189, 0.002], [219, 0.002], [222, 0.002], [224, 0.002], [230, 0.002], [250, 0.002], [271, 0.002], [305, 0.002], [308, 0.002], [346, 0.002], [354, 0.002], [361, 0.002], [394, 0.002], [419, 0.002], [422, 0.002], [434, 0.002], [461, 0.002], [484, 0.002], [514, 0.002], [541, 0.002], [547, 0.002], [559, 0.002], [572, 0.002], [582, 0.002], [621, 0.002], [625, 0.002], [626, 0.002], [677, 0.002], [712, 0.002], [735, 0.002], [813, 0.002], [818, 0.002], [876, 0.002], [994, 0.002], [1017, 0.002], [1019, 0.002], [1045, 0.002], [1092, 0.002], [1112, 0.002], [1487, 0.002], [1618, 0.002], [1634, 0.002], [2404, 0.002], [31, 0.001], [66, 0.001], [91, 0.001], [96, 0.001], [98, 0.001], [114, 0.001], [121, 0.001], [131, 0.001], [149, 0.001], [159, 0.001], [178, 0.001], [228, 0.001], [249, 0.001], [260, 0.001], [275, 0.001], [284, 0.001], [287, 0.001], [315, 0.001], [329, 0.001], [335, 0.001], [342, 0.001], [364, 0.001], [366, 0.001], [380, 0.001], [409, 0.001], [412, 0.001], [420, 0.001], [427, 0.001], [433, 0.001], [447, 0.001], [457, 0.001], [481, 0.001], [515, 0.001], [535, 0.001], [548, 0.001], [557, 0.001], [565, 0.001], [583, 0.001], [600, 0.001], [612, 0.001], [631, 0.001], [633, 0.001], [747, 0.001], [774, 0.001], [781, 0.001], [796, 0.001], [798, 0.001], [811, 0.001], [829, 0.001], [870, 0.001], [926, 0.001], [938, 0.001], [984, 0.001], [990, 0.001], [1044, 0.001], [1049, 0.001], [1053, 0.001], [1076, 0.001], [1104, 0.001], [1124, 0.001], [1138, 0.001], [1221, 0.001], [1477, 0.001], [1522, 0.001], [1538, 0.001], [1693, 0.001], [1734, 0.001], [1736, 0.001], [1867, 0.001], [1871, 0.001], [2059, 0.001], [2165, 0.001]]
real 488m24.633s
user 478m50.925s
sys 1m19.903s
Macbook Pro 2010 C2D Python
numDecisionTrees = 1000,
treeSplitCriterion='informationGain',
pruneAggressiveness = 0.9,
highErrorTreeDiscardThreshold = 1,
optimumFeatureSubsetSelectionCriteria = 'log2',
featureStandardDeviationThreshold= 0.1
numCorrect 65
numSamplesPerFeatures: 0.0350669818755
forrestErrorRate: 0.269662921348
globalVariableRanks: [[9, 0.212], [64, 0.138], [14, 0.093], [102, 0.082], [18, 0.072], [75, 0.072], [77, 0.072], [90, 0.069], [0, 0.066], [12, 0.063], [8, 0.062], [152, 0.06], [3, 0.051], [28, 0.049], [48, 0.049], [126, 0.043], [269, 0.041], [6, 0.039], [62, 0.038], [179, 0.038], [23, 0.036], [39, 0.036], [7, 0.035], [15, 0.035], [63, 0.035], [19, 0.034], [27, 0.034], [59, 0.034], [552, 0.034], [17, 0.033], [203, 0.032], [205, 0.032], [215, 0.032], [32, 0.031], [465, 0.031], [65, 0.03], [202, 0.03], [30, 0.029], [158, 0.029], [214, 0.029], [286, 0.029], [51, 0.028], [130, 0.028], [29, 0.027], [176, 0.027], [24, 0.026], [99, 0.026], [41, 0.025], [128, 0.025], [10, 0.024], [34, 0.024], [49, 0.024], [89, 0.024], [1, 0.023], [5, 0.023], [47, 0.023], [57, 0.023], [69, 0.022], [33, 0.021], [40, 0.021], [244, 0.021], [251, 0.02], [295, 0.02], [160, 0.019], [216, 0.019], [4, 0.018], [53, 0.018], [72, 0.018], [144, 0.018], [254, 0.018], [21, 0.017], [125, 0.017], [70, 0.016], [382, 0.016], [2, 0.015], [16, 0.015], [52, 0.015], [54, 0.015], [86, 0.015], [117, 0.015], [707, 0.015], [13, 0.014], [105, 0.014], [187, 0.014], [319, 0.014], [71, 0.013], [83, 0.013], [94, 0.013], [154, 0.013], [461, 0.013], [145, 0.012], [304, 0.012], [44, 0.011], [60, 0.011], [136, 0.011], [190, 0.011], [220, 0.011], [280, 0.011], [547, 0.011], [58, 0.01], [88, 0.01], [93, 0.01], [111, 0.01], [161, 0.01], [463, 0.01], [622, 0.01], [85, 0.009], [112, 0.009], [272, 0.009], [37, 0.008], [38, 0.008], [43, 0.008], [81, 0.008], [171, 0.008], [226, 0.008], [229, 0.008], [239, 0.008], [334, 0.008], [359, 0.008], [387, 0.008], [476, 0.008], [1002, 0.008], [25, 0.007], [180, 0.007], [300, 0.007], [355, 0.007], [514, 0.007], [569, 0.007], [575, 0.007], [22, 0.006], [76, 0.006], [82, 0.006], [119, 0.006], [138, 0.006], [151, 0.006], [211, 0.006], [228, 0.006], [270, 0.006], [606, 0.006], [634, 0.006], [859, 0.006], [84, 0.005], [87, 0.005], [135, 0.005], [284, 0.005], [348, 0.005], [413, 0.005], [506, 0.005], [511, 0.005], [531, 0.005], [652, 0.005], [787, 0.005], [50, 0.004], [61, 0.004], [74, 0.004], [97, 0.004], [108, 0.004], [121, 0.004], [168, 0.004], [198, 0.004], [201, 0.004], [207, 0.004], [230, 0.004], [276, 0.004], [358, 0.004], [376, 0.004], [481, 0.004], [717, 0.004], [1421, 0.004], [46, 0.003], [55, 0.003], [66, 0.003], [68, 0.003], [124, 0.003], [132, 0.003], [153, 0.003], [237, 0.003], [240, 0.003], [261, 0.003], [278, 0.003], [308, 0.003], [346, 0.003], [431, 0.003], [447, 0.003], [497, 0.003], [530, 0.003], [612, 0.003], [674, 0.003], [755, 0.003], [800, 0.003], [1092, 0.003], [1666, 0.003], [56, 0.002], [110, 0.002], [113, 0.002], [116, 0.002], [146, 0.002], [147, 0.002], [149, 0.002], [162, 0.002], [167, 0.002], [174, 0.002], [188, 0.002], [221, 0.002], [234, 0.002], [241, 0.002], [258, 0.002], [265, 0.002], [271, 0.002], [298, 0.002], [299, 0.002], [316, 0.002], [388, 0.002], [404, 0.002], [422, 0.002], [428, 0.002], [444, 0.002], [565, 0.002], [572, 0.002], [655, 0.002], [660, 0.002], [668, 0.002], [684, 0.002], [696, 0.002], [745, 0.002], [761, 0.002], [772, 0.002], [822, 0.002], [893, 0.002], [906, 0.002], [915, 0.002], [951, 0.002], [967, 0.002], [1012, 0.002], [1104, 0.002], [1221, 0.002], [1790, 0.002], [2378, 0.002], [20, 0.001], [42, 0.001], [80, 0.001], [91, 0.001], [101, 0.001], [107, 0.001], [122, 0.001], [139, 0.001], [148, 0.001], [166, 0.001], [192, 0.001], [197, 0.001], [204, 0.001], [212, 0.001], [224, 0.001], [242, 0.001], [255, 0.001], [275, 0.001], [306, 0.001], [340, 0.001], [352, 0.001], [354, 0.001], [361, 0.001], [366, 0.001], [405, 0.001], [418, 0.001], [419, 0.001], [434, 0.001], [438, 0.001], [445, 0.001], [462, 0.001], [471, 0.001], [472, 0.001], [494, 0.001], [499, 0.001], [500, 0.001], [508, 0.001], [526, 0.001], [533, 0.001], [591, 0.001], [607, 0.001], [616, 0.001], [618, 0.001], [626, 0.001], [630, 0.001], [631, 0.001], [641, 0.001], [654, 0.001], [711, 0.001], [712, 0.001], [716, 0.001], [720, 0.001], [732, 0.001], [735, 0.001], [756, 0.001], [768, 0.001], [775, 0.001], [803, 0.001], [814, 0.001], [816, 0.001], [828, 0.001], [866, 0.001], [867, 0.001], [879, 0.001], [892, 0.001], [905, 0.001], [994, 0.001], [1003, 0.001], [1053, 0.001], [1054, 0.001], [1066, 0.001], [1071, 0.001], [1079, 0.001], [1088, 0.001], [1103, 0.001], [1112, 0.001], [1138, 0.001], [1146, 0.001], [1155, 0.001], [1180, 0.001], [1442, 0.001], [1522, 0.001], [1668, 0.001], [1796, 0.001], [1871, 0.001], [2082, 0.001], [2316, 0.001]]
real 313m19.429s
user 309m15.741s
sys 0m34.250s
- When setting
treeSplitCriteriontoinformationGain, it seems be giving better accuracy than setting it togainRatiofor this dataset. - Individual trees tend to have a very high error rate (e.g. upto 70%) in this dataset, but when they are bagged, forrest wide error rate tend to drop significantly (upto 20%). This is great example of the effect of bagging on noisy dataset.
- The Inpatient dataset has a value of numSamplesPerFeatures of 0.22, for Outpatient it's 0.18 whereas the HummanCRC (Cancer) dataset has a value of 0.035. This might be a reason for the observed low accuracy for this dataset.