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notes.txt
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mae 0.172126
medae 0.260859
rmse 0.114699
dataset.csv
0-20k
mae 0.133261
medae 0.099732
rmse 0.106227
20k-40k
mae 0.080765
medae -0.081400
rmse 0.118345
0-40k, 100 estimators
mae -0.020205
medae -0.129283
rmse -0.014245
min_split_size {
-Split 50
mae -0.037213
medae -0.312342
rmse 0.017490
-Split 100
mae -0.016510
medae -0.306533
rmse 0.050852
-Split 200
mae -0.031677
medae -0.310952
rmse 0.034808
}
features {
+dayOfWeek
mae -0.018364
medae -0.137587
rmse -0.010391
secondsToArrival
mae 0.055420
medae 0.038286
rmse -0.004819
secondsToArrival,dayOfWeek
mae 0.057374
medae 0.040263
rmse -0.000548
secondsToArrival,dayOfWeek,distance
mae 0.082552
medae 0.070000
rmse 0.021599
secondsToArrival,dayOfWeek,distance,kmperhr
mae 0.083497
medae 0.082867
rmse 0.022859
secondsToArrival,dayOfWeek,distance,kmperhr,layover
mae 0.089138
medae 0.076085
rmse 0.027700
secondsToArrival,dayOfWeek,distance,kmperhr,isDeparture
mae 0.084397
medae 0.076595
rmse 0.023037
secondsToArrival,dayOfWeek,distance,kmperhr,minutesIntoDay
mae -0.023848
medae -0.137276
rmse -0.016921
secondsToArrival,dayOfWeek,distance,kmperhr,morningRush
mae 0.083323
medae 0.081004
rmse 0.022183
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush
mae 0.083579
medae 0.083530
rmse 0.022230
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,classMode
mae 0.067118
medae 0.121400
rmse -0.022389
}
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush
0-80k, 100 estimators
mae 0.101692
medae 0.126955
rmse -0.050491
features {
secondsToArrival,distance,kmperhr,eveningRush
mae 0.100174
medae 0.127609
rmse -0.053185
secondsToArrival
mae 0.056229
medae 0.068296
rmse -0.078331
secondsToArrival,distance,kmperhr,eveningRush,minutesToArrival
mae -0.053728
medae 0.026467
rmse -0.176538
}
min_split_size {
Split 500
}
0-160k, 10 estimators
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush
mae -0.077095
medae -0.079785
rmse -0.107362
max_samples {
.5
mae -0.046839
medae -0.046308
rmse -0.083215
.2, 10 estimators
mae -0.029311
medae -0.073548
rmse -0.043360
.2, 100 estimators
mae 0.003441
medae -0.008232
rmse -0.018493
.2, 100 estimators, secondsToArrival
mae 0.017358
medae 0.036310
rmse -0.052024
.1, 100 estimators,
mae 0.016274
medae -0.020065
rmse 0.006333
.1, 200 estimators,
mae 0.021364
medae -0.012769
rmse 0.008458
.1, 300 estimators,
mae 0.017417
medae -0.019952
rmse 0.004956
1000 samples, 200 estimators
mae 0.038959
medae -0.068108
rmse 0.056215
1000 samples, 400 estimators
mae 0.043670
medae -0.069599
rmse 0.062818
1000 samples, 400 estimators, secondsToArrival
mae 0.065604
medae -0.006086
rmse 0.066491
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,humidity
mae 0.029310
medae -0.079118
rmse 0.057459
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure
mae 0.082159
medae -0.007263
rmse 0.092297
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure,morningRush
mae 0.076753
medae -0.016124
rmse 0.087606
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure,wind
mae 0.059045
medae -0.075495
rmse 0.097079
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure,temperature
mae 0.023381
medae -0.103153
rmse 0.066732
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure,visibility
mae 0.079883
medae -0.013952
rmse 0.090120
1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure,busID
mae 0.069503
medae -0.022790
rmse 0.084391
}
0-320k, 1000 samples, 400 estimators, 1000 samples, 400 estimators, secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush
mae 0.110417
medae -0.026820
rmse 0.076320
{
2000 samples, 400 estimators
mae 0.11708564142
medae -0.02419871794
rmse 0.10517122712
4000 samples, 400 estimators
mae 0.11832
medae -0.016257
rmse 0.108568
8000 samples, 400 estimators
mae 0.113122
medae -0.015214
rmse 0.109644
All
(max_samples=3000, n_estimators=400)
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush
{'mae': 182.19415500110929, 'medae': 101.33000000000001, 'rmse': 367.9542994493536}
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,pressure
{196, 115, 392}
secondsToArrival,dayOfWeek,distance,kmperhr,eveningRush,minutesIntoDay
Bad
secondsToArrival,dayOfWeek,distance,kmperhr,minutesIntoDay
{'mae': 178.40064775117685, 'medae': 95.638749999999987, 'rmse': 363.08774338811565}
secondsToArrival,distance,kmperhr,minutesIntoDay
{'mae': 167.943284856394, 'medae': 86.048749999999984, 'rmse': 358.8260266481942}
secondsToArrival,distance,kmperhr,minutesIntoDay,layover
{'mae': 167.31292921174591, 'medae': 85.783749999999998, 'rmse': 358.3976251490235}
(max_samples=3000, n_estimators=75)
secondsToArrival,distance,kmperhr,minutesIntoDay
{177,89,386}
secondsToArrival,distance,minutesIntoDay,layover
{'mae': 168.65406469070786, 'medae': 87.146666666666647, 'rmse': 358.28333669961796}
secondsToArrival,kmperhr,minutesIntoDay,layover
Bad
secondsToArrival,distance,minutesIntoDay,layover,isDeparture
Bad
secondsToArrival,distance,minutesIntoDay,layover,classMode
Bad
(max_samples=1000, n_estimators=75)
secondsToArrival,distance,minutesIntoDay,layover
{'mae': 170.78765390115021, 'medae': 88.74666666666667, 'rmse': 365.5547240992596}
(max_samples=5000, n_estimators=75)
secondsToArrival,distance,minutesIntoDay,layover
{'mae': 166.7952535371152, 'medae': 84.333333333333371, 'rmse': 356.5755381620684}
(max_samples=10000, n_estimators=75)
{'mae': 165.90029892004023, 'medae': 83.360000000000014, 'rmse': 354.0919949619977}
(max_samples=32000, n_estimators=75)
{'mae': 163.17913550318505, 'medae': 79.399999999999977, 'rmse': 347.7779449628575}
(max_samples=50000, n_estimators=75)
{'mae': 163.1159747707103, 'medae': 77.806666666666729, 'rmse': 349.56821118414155}
(max_samples=50000, n_estimators=150)
{'mae': 161.93836866535068, 'medae': 77.25333333333333, 'rmse': 347.41317199015197}