Variable Importance
Dashboard options
- Maximum variables in Variable Importance
- Display boxplots over Variable Importance
- Left margin for variables names with values
Connector options
# Size of random subset. For large datasets can speedup calculations.
# Use None to select whole dataset.
arena.set_option('VariableImportance', 'N', None)
# Number of permutations.
arena.set_option('VariableImportance', 'B', 10)Partial Dependence
Dashboard options
- Left margin for variables values - for categorical variables
Connector options
# Method of selecting points: 'quantile' or 'uniform'
arena.set_option('PartialDependence', 'grid_type', 'quantile')
# Maximum number of points for profile. Final number can
# be lower using 'quantile' method.
arena.set_option('PartialDependence', 'grid_points', 101)
# Size of random subset. For large datasets can speedup calculations.
# Use None to select whole dataset.
arena.set_option('PartialDependence', 'N', 500)Accumulated Dependence
Dashboard options
- Left margin for variables values - for categorical variables
Connector options
# Method of selecting points: 'quantile' or 'uniform'
arena.set_option('AccumulatedDependence', 'grid_type', 'quantile')
# Maximum number of points for profile. Final number can
# be lower using 'quantile' method.
arena.set_option('AccumulatedDependence', 'grid_points', 101)
# Size of random subset. For large datasets can speedup calculations.
# Use None to select whole dataset.
arena.set_option('AccumulatedDependence', 'N', 500)Shapley Values Dependence
Shapley Values were described in the page about observation level charts. In this chart we try to visualise how they depend on value of variable.
Dashboard options
- Left margin for variables values - for categorical variables
- Display error bars over Shapley dependence points - for numerical variables
- Shapley dependence jitter range as per mill of chart range - for numerical variables
Connector options
# Size of random subset.
arena.set_option('DatasetShapleyValues', 'N', 500)
# Number of permutations for each observation.
# For best utilization thus should be multiplicity of parallel processes.
arena.set_option('DatasetShapleyValues', 'B', 4)
# Number of parallel processes
arena.set_option('DatasetShapleyValues', 'cpus', 4)Shapley Values Variable Importance
Shapley Values can be used as a measure of importance. For this chart, we assume that important variables are those with large absolute contributions to the final prediction.
Dashboard options
- Display boxplots over Variable Importance
- Maximum variables in Variable Importance
- Left margin for variables names with values
Connector options
# Size of random subset.
arena.set_option('DatasetShapleyValues', 'N', 500)
# Number of permutations for each observation.
# For best utilization thus should be multiplicity of parallel processes.
arena.set_option('DatasetShapleyValues', 'B', 4)
# Number of parallel processes
arena.set_option('DatasetShapleyValues', 'cpus', 4)