Benchopt
Chart type
Objective curve
Bar chart
Boxplot
Table
Scale
linear
semilog-y
semilog-x
loglog
log
Quantiles
Suboptimal Curve
Relative Curve
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
objective_column
objective_value
X_axis
Time
Iteration
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
objective_column
objective_value
minimize
True
False
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
objective_column
objective_value
X_axis
Solver
Iteration
Y_axis
Time
Objective Metric
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
Save as view
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Chart type
objective_curve
bar_chart
boxplot
table
Scale
linear
semilog-y
semilog-x
loglog
log
Quantiles
Suboptimal Curve
Relative Curve
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
objective_column
objective_value
X_axis
Time
Iteration
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
objective_column
objective_value
minimize
True
False
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
objective_column
objective_value
X_axis
Solver
Iteration
Y_axis
Time
Objective Metric
dataset
Simulated[n_samples=100,n_features=50]
Simulated[n_samples=1000,n_features=200]
objective
L1-regularized Quantile Regression[reg=0.05,quantile=0.2]
L1-regularized Quantile Regression[reg=0.05,quantile=0.5]
L1-regularized Quantile Regression[reg=0.05,quantile=0.8]
L1-regularized Quantile Regression[reg=0.1,quantile=0.2]
L1-regularized Quantile Regression[reg=0.1,quantile=0.5]
L1-regularized Quantile Regression[reg=0.1,quantile=0.8]
L1-regularized Quantile Regression[reg=0.5,quantile=0.2]
L1-regularized Quantile Regression[reg=0.5,quantile=0.5]
L1-regularized Quantile Regression[reg=0.5,quantile=0.8]
Save as view
Result on L1-regularized Quantile Regression
CPU : 72
RAM (GB) : 376
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System information
CPU
: 72
RAM (GB)
: 376
platform
: Linux4.15.0-139-generic-x86_64
processor
: Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz
nb threads
: 1
numpy
: 1.18.5 blas=openblas lapack=openblas
scipy
: 1.5.2
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