HGB fit-time breakdown (thread scalability)

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (pypi)
numpy 2.4.6 (conda-forge)
scipy 1.17.1 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (conda-forge)
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn @ 27d3e13ba
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (128 threads)
libomp (344 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn @ 6aa2f57c2
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (128 threads)
libomp (344 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn @ 27d3e13ba
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn @ 44f1cbbde
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn @ 6aa2f57c2
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (conda-forge)
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libmkl_rt 2026.0-Product (172 threads)
libomp (344 threads)

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (conda-forge)
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libomp (344 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (conda-forge)
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libmkl_rt 2026.1-Product (16 threads)
libomp (16 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
GOMP_SPINCOUNT: 300000
KMP_BLOCKTIME: 0ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 27d3e13ba
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (16 threads)
libomp (16 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
GOMP_SPINCOUNT: 300000
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 27d3e13ba
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (16 threads)
libomp (16 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
KMP_BLOCKTIME: 0ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 6aa2f57c2
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (16 threads)
libomp (16 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
GOMP_SPINCOUNT: 300000
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 6aa2f57c2
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (16 threads)
libomp (16 threads)

OpenMP

Intel/LLVM OpenMP (spec 201611)
KMP_BLOCKTIME: 0ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 27d3e13ba
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 27d3e13ba
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 1

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 6aa2f57c2
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000
KMP_BLOCKTIME: 200ms

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn @ 6aa2f57c2
numpy 2.5.0 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 1

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (pypi)
numpy 2.5.2 (conda-forge)
scipy 1.18.0 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.34 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

bin fitbin transformother / unmeasuredapply splitfind splitcompute hist

x-axis: requested threads (OMP_NUM_THREADS) - in parens, the actual thread count used to grow trees (absent on records without that instrumentation)

XS (1,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

S (1,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

S-thin (5,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

M (20,000 x 50)

n_iter=100, max_leaf_nodes=31, max_features=0.5

amazon_employee_access (26,215 x 9)

n_iter=150, max_leaf_nodes=31, 9 categorical, min_samples_leaf=20

kddcup09_churn (40,000 x 207)

n_iter=150, max_leaf_nodes=15, 34 categorical, min_samples_leaf=50

M-very-thin (50,000 x 5)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-leaf255 (100,000 x 100)

n_iter=20, max_leaf_nodes=255, max_features=0.5

L (100,000 x 100)

n_iter=100, max_leaf_nodes=31, max_features=0.5

M-stumps (200,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

year_prediction_msd (463,810 x 90)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5

covtype (464,809 x 12)

n_iter=200, max_leaf_nodes=127, 2 categorical, min_samples_leaf=5

L-thin (500,000 x 10)

n_iter=100, max_leaf_nodes=31, max_features=1.0

L-stumps (1,000,000 x 100)

n_iter=100, max_leaf_nodes=2, max_features=0.5

susy (4,500,000 x 18)

n_iter=200, max_leaf_nodes=127, min_samples_leaf=5