result.txt

featureUpdate6:四层数据，时序波动特征，中心20*20个点特征
max_depth = 3
n_estimators=100
test pred:12.623681681623
train pred:11.573620871432695
n_estimators=150
test pred:12.317659830997428
train pred:10.720445544796865
n_estimators=180
test pred:12.208551919586299
train pred:10.286177731502873
n_estimators=200
test pred:12.145938939596897
train pred:9.990710964429967
n_estimators=300
test pred:11.956212055111502
train pred:8.877026045679548

#
featureUpdate6 learning_rate=0.2
n_estimators=100
test pred:12.403705478463547
train pred:9.964415337003672
n_estimators=150
test pred:12.211558197477899
train pred:8.820394682794719
n_estimators=180
test pred:12.134433143248971
train pred:8.272877542770784
n_estimators=200
test pred:12.099284451670718
train pred:7.955848401303026
n_estimators=300
test pred:11.868172598526131
train pred:6.596863362624191

#
featureUpdate2 learning_rate=0.2
n_estimators=100
test pred:12.70641711939246
train pred:10.431358393102265
n_estimators=150
test pred:12.459133188146383
train pred:9.097374177122942
n_estimators=180
test pred:12.371916719027164
train pred:8.465162521131331
n_estimators=200
test pred:12.318136914944857
train pred:8.090355076936463
n_estimators=300
test pred:12.183165433923184
train pred:6.503274517322605

#
random forest 20
test pred:12.104646685260995
train pred:4.690074445038159

#featureUpdate7
n_estimators=100
test pred:12.511569266405072
train pred:11.468932697550247

#featureUpdate8
n_estimators=100
test pred:12.598992289426565
train pred:11.52581011319261
n_estimators=150
test pred:12.372631817875744
train pred:10.73342254237062
n_estimators=180
test pred:12.245633177402098
train pred:10.295530853961504
n_estimators=200
test pred:12.203993002151494
train pred:10.01964103101855
n_estimators=300
test pred:11.955014968315712
train pred:8.896421731556167
11.0121736589

#
feature_update_train7 max = 2
n_estimators=85
learning_rate=0.1
test pred:13.145149877814049
train pred:13.043230214878472
n_estimators=90
learning_rate=0.1
test pred:13.11939406310709
train pred:12.9966327341424
n_estimators=100
learning_rate=0.1
test pred:13.065093535271114
train pred:12.894628182113141
n_estimators=120
learning_rate=0.1
test pred:12.996149018972739
train pred:12.63867189456957

#featureUpdate9 max =2
n_estimators=90
learning_rate=0.1
test pred:13.113375581148345
train pred:12.997301441807453
n_estimators=100
learning_rate=0.1
test pred:13.063530048223345
train pred:12.906313042863461
n_estimators=120
learning_rate=0.1
test pred:12.998649518223969
train pred:12.655903549975468

#
feature_update_train7 max = 2 80
learning_rate=0.1
test pred:13.154279394817765
train pred:13.163041065271548

#
feature_update_train9 max = 2
n_estimators=60
learning_rate=0.1
test pred:13.284394579922454
train pred:13.450612394192735
n_estimators=50
learning_rate=0.1
test pred:13.351583503255181
train pred:13.6749400911764
n_estimators=40
learning_rate=0.1
test pred:13.495396256673521
train pred:13.9056013769743

#
feature_update_train9 max = 3
n_estimators=40
learning_rate=0.1
test pred:13.00302623825569
train pred:12.948496769442505
n_estimators=36
learning_rate=0.1
test pred:13.052810680081569
train pred:13.067498235937808
n_estimators=30
learning_rate=0.1
test pred:13.159532377700373
train pred:13.296807942161482
n_estimators=20
learning_rate=0.1
test pred:13.4241057072059
train pred:13.844570380205843
n_estimators=10
learning_rate=0.1
test pred:13.930656800771468
train pred:14.58340923946873

#featureUpdate10_2
n_estimators=50
test pred:13.381558185397957
train pred:13.637190443866292
n_estimators=60
test pred:13.268645827169548
train pred:13.408712900830752
n_estimators=70
test pred:13.17660440527657
train pred:13.256819740521971
n_estimators=80
test pred:13.095511516784121
train pred:13.09926500685547
n_estimators=100
test pred:12.962584924444837
train pred:12.848624720657144

#featureUpdate10_3
n_estimators=50
max_depth=2
test pred:13.28625313790449     score 13.6
train pred:13.625511265589333
n_estimators=60
max_depth=2
test pred:13.165629560197472
train pred:13.396882279747524
n_estimators=70
max_depth=2
test pred:13.064558161833299
train pred:13.241448676505621
n_estimators=80
max_depth=2
test pred:13.00309265025689
train pred:13.071901631692475
n_estimators=90
max_depth=2
test pred:12.955022540783034
train pred:12.944752629141579
n_estimators=100
max_depth=2
test pred:12.902901751730457
train pred:12.845060491232283

#featureUpdate10_4
n_estimators=50
test pred:13.332917824364422
train pred:13.596363229445517
n_estimators=60
test pred:13.213487528079883
train pred:13.396176165439513
n_estimators=70
test pred:13.10819335098783
train pred:13.232742431772904
n_estimators=80
test pred:13.028332928142436
train pred:13.05872852961913
n_estimators=100
test pred:12.86919017601814
train pred:12.791915988739184

#featureUpdate9_copy
(17770, 3261)
max_depth=3
estimators=150
velify:
12.5334900921
train:
11.3776224998
estimators=200
velify:
11.550748202
train:
10.1344264144
max_depth=4
estimators=150
velify:
10.1836276086
train:
8.34158042085
estimators=200
velify:
9.37591981994
train:
7.20248574924

(12331, 3261)
[1109 1648 6771 ...,  603 8275 1578]
max_depth=3
estimators=150
velify:
10.6453847159
train:
11.2729932001
estimators=200
velify:
9.9414817475
train:
10.389250062
max_depth=4
estimators=150
velify:
8.75527324868
train:
8.97797705027
estimators=200
velify:
7.86024826823
train:
7.88008217996