Integration with xgboost python package gives the following readings

The xgBoost ensemble of trees was generated for four depths ( and this resulted in varying number of trees ). The readings are given for all four of these modelling configurations

- 2012 Macbook Pro (2.6 GHz Intel Core i7 with 16GB RAM), No GPU was enabled for either modelling or scoring
- The model was to perform iris data set recognition
- The source code for the modelling and the binary version of the model can be located in the resources folder of the git project ( link in the second comment )
- Readings in microseconds

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth3.testXGBoostPredictIrisDepth3 ( **60 trees** )":

**475.027 ±(99.9%) 5.441 us/op [Average]**

(min, avg, max) = (428.774, 475.027, 567.648), stdev = 23.037

CI (99.9%): [469.586, 480.468] (assumes normal distribution)

- Run complete. Total time: 00:08:28

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth3.testXGBoostPredictIrisDepth3 avgt 200 475.027 ± 5.441 us/op

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth9.testXGBoostPredictIrisDepth9 ( **120 trees** )":

**479.907 ±(99.9%) 6.342 us/op [Average]**

(min, avg, max) = (427.637, 479.907, 576.946), stdev = 26.852

CI (99.9%): [473.565, 486.249] (assumes normal distribution)

- Run complete. Total time: 00:08:31

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth9.testXGBoostPredictIrisDepth9 avgt 200 479.907 ± 6.342 us/op

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth27.testXGBoostPredictIrisDepth27 ( **300 trees** )":

**524.516 ±(99.9%) 13.392 us/op [Average]**

(min, avg, max) = (423.894, 524.516, 838.232), stdev = 56.701

CI (99.9%): [511.124, 537.908] (assumes normal distribution)

- Run complete. Total time: 00:08:30

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth27.testXGBoostPredictIrisDepth27 avgt 200 524.516 ± 13.392 us/op

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth125.testXGBoostPredictIrisDepth125 ( **900 trees** )":

**519.460 ±(99.9%) 10.647 us/op [Average]**

(min, avg, max) = (458.625, 519.460, 693.956), stdev = 45.082

CI (99.9%): [508.812, 530.107] (assumes normal distribution)

- Run complete. Total time: 00:08:35

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth125.testXGBoostPredictIrisDepth125 avgt 200 519.460 ± 10.647 us/op

Integration with xgboost python package gives the following readings

The xgBoost ensemble of trees was generated for four depths ( and this resulted in varying number of trees ). The readings are given for all four of these modelling configurations

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth3.testXGBoostPredictIrisDepth3 (

60 trees)":475.027 ±(99.9%) 5.441 us/op [Average](min, avg, max) = (428.774, 475.027, 567.648), stdev = 23.037

CI (99.9%): [469.586, 480.468] (assumes normal distribution)

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth3.testXGBoostPredictIrisDepth3 avgt 200 475.027 ± 5.441 us/op

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth9.testXGBoostPredictIrisDepth9 (

120 trees)":479.907 ±(99.9%) 6.342 us/op [Average](min, avg, max) = (427.637, 479.907, 576.946), stdev = 26.852

CI (99.9%): [473.565, 486.249] (assumes normal distribution)

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth9.testXGBoostPredictIrisDepth9 avgt 200 479.907 ± 6.342 us/op

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth27.testXGBoostPredictIrisDepth27 (

300 trees)":524.516 ±(99.9%) 13.392 us/op [Average](min, avg, max) = (423.894, 524.516, 838.232), stdev = 56.701

CI (99.9%): [511.124, 537.908] (assumes normal distribution)

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth27.testXGBoostPredictIrisDepth27 avgt 200 524.516 ± 13.392 us/op

Result "github.ananthc.sampleapps.apex.xgboost.XGBoostJepBenchMarkDepth125.testXGBoostPredictIrisDepth125 (

900 trees)":519.460 ±(99.9%) 10.647 us/op [Average](min, avg, max) = (458.625, 519.460, 693.956), stdev = 45.082

CI (99.9%): [508.812, 530.107] (assumes normal distribution)

Benchmark Mode Cnt Score Error Units

XGBoostJepBenchMarkDepth125.testXGBoostPredictIrisDepth125 avgt 200 519.460 ± 10.647 us/op