Ceteris Paribus

Dashboard options
- Left margin for variables values - for categorical variables only
Connector options
# Maxiumum number of grid points.
# When grid_type is quantile then actual number can be lower.
arena.set_option('CeterisParibus', 'grid_points', value)
# Type of grid. Points can be taken uniformly ('uniform')
# or from empirical distribution of the variable ('quantile')
arena.set_option('CeterisParibus', 'grid_type', value)
Break Down

- Choose path - we want to start with variables that were the most important. To create an order we for each column set the observation's value for the whole testing dataset. The absolute value of the difference in averaged prediction will be our importance.
- Calculate contributions along the path - this step looks similar to the previous one. We start with the testing dataset and iterate over the path. Each iteration fixes column at observation's value. The next iteration uses the data frame from the previous one. In the end, every column will be constant. After replacements, we measure contribution as a difference in averaged prediction.
Dashboard options
- Left margin for variables names with values
- Maximum variables in Break Down
Shapley Values

Dashboard options
- Display boxplots over Shapley Values
- Left margin for variables names with values
- Maximum variables in Shapley Values
Connector options
# Number of random paths
arena.set_option('ShapleyValues', 'B', value)
# Number of parallel processes
arena.set_option('ShapleyValues', 'cpus', value)