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Chart type
Objective curve
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Scale
linear
semilog-y
semilog-x
loglog
log
Quantiles
Suboptimal Curve
Relative Curve
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
objective_column
objective_value
X_axis
Time
Iteration
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
objective_column
objective_value
minimize
True
False
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
objective_column
objective_value
X_axis
Solver
Iteration
Y_axis
Time
Objective Metric
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
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=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
objective_column
objective_value
X_axis
Time
Iteration
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
objective_column
objective_value
minimize
True
False
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
objective_column
objective_value
X_axis
Solver
Iteration
Y_axis
Time
Objective Metric
dataset
Simulated[n_samples=100,n_features=5000]
Simulated[n_samples=100,n_features=10000]
Simulated[n_samples=1000,n_features=10]
objective
Sparse Logistic Regression[fit_intercept=False,reg=0.05]
Sparse Logistic Regression[fit_intercept=False,reg=0.1]
Sparse Logistic Regression[fit_intercept=False,reg=0.5]
Save as view
Result on Sparse Logistic Regression
CPU : 16
RAM (GB) : 32
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System information
CPU
: 16
RAM (GB)
: 32
platform
: Darwin19.0.0-x86_64
processor
: Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz
numpy
: 1.19.4 blas=NO_ATLAS_INFO lapack=lapack
scipy
: 1.6.2
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