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=5000]
Simulated[n_samples=100,n_features=10000]
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]
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]
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]
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]
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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]
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]
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]
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]
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
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