Benchopt
Dataset:
Simulated[n_features=50,n_samples=100]
Simulated[n_features=200,n_samples=1000]
Objective:
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.2,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.2,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.5,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.5,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.8,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.8,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.2,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.2,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.5,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.5,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.8,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.8,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.2,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.2,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.5,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.5,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.8,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.8,reg=0.05]
Objective column
objective_value
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
Scale
semilog-y
semilog-x
loglog
linear
Quantiles
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Dataset:
Simulated[n_features=50,n_samples=100]
Simulated[n_features=200,n_samples=1000]
Objective:
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.2,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.2,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.5,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.5,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.8,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.8,reg=0.5]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.2,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.2,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.5,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.5,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.8,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.8,reg=0.1]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.2,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.2,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.5,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.5,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=True,quantile=0.8,reg=0.05]
L1-regularized Quantile Regression[fit_intercept=False,quantile=0.8,reg=0.05]
Objective column
objective_value
Chart type
objective_curve
suboptimality_curve
relative_suboptimality_curve
bar_chart
Scale
semilog-y
semilog-x
loglog
linear
Quantiles
Result on quantile regression benchmark
CPU : 20
RAM (GB) : 31
CUDA : NVIDIA T600 Laptop GPU: cuda_11.6
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Solvers
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System information
CPU
: 20
RAM (GB)
: 31
CUDA
: NVIDIA T600 Laptop GPU: cuda_11.6
platform
: Linux5.14.0-1054-oem-x86_64
processor
: 12th Gen Intel(R) Core(TM) i7-12700H
nb threads
: 1
numpy
: 1.23.5
scipy
: 1.9.3
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