Break Down Plot

Break Down Plot

# S3 method for broken
plot(x, trans = I, ..., top_features = 0,
  min_delta = 0, add_contributions = TRUE, vcolors = c(`-1` =
  "#f05a71", `0` = "#371ea3", `1` = "#8bdcbe", X = "#371ea3"),
  digits = 3, rounding_function = round, plot_distributions = FALSE)

Arguments

x	the model model of 'broken' class
trans	transformation that shal be applied to scores
...	other parameters
top_features	maximal number of variables from model we want to plot
min_delta	minimal stroke value of variables from model we want to plot
add_contributions	shall variable contributions to be added on plot?
vcolors	named vector with colors
digits	number of decimal places (round) or significant digits (signif) to be used. See the `rounding_function` argument
rounding_function	function that is to used for rounding numbers. It may be `signif()` which keeps a specified number of significant digits. Or the default `round()` to have the same precision for all components
plot_distributions	if TRUE then distributions of conditional propotions will be plotted. This requires keep_distributions=TRUE in the broken.default().

Value

a ggplot2 object

Examples

library("breakDown")
library("randomForest")
library("ggplot2")
set.seed(1313)
model <- randomForest(factor(left)~., data = HR_data, family = "binomial", maxnodes = 5)
predict.function <- function(model, new_observation)
      predict(model, new_observation, type="prob")[,2]
predict.function(model, HR_data[11,-7])
#> [1] 0.888
explain_1 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "down")
explain_1
#>                              contribution
#> (Intercept)                         0.148
#> - satisfaction_level = 0.45         0.133
#> - number_project = 2                0.201
#> - last_evaluation = 0.54            0.182
#> - average_montly_hours = 135        0.141
#> - time_spend_company = 3            0.068
#> - Work_accident = 0                 0.010
#> - salary = low                      0.005
#> - sales = sales                     0.000
#> - promotion_last_5years = 0         0.000
#> final_prognosis                     0.888
#> baseline:  0 
plot(explain_1) + ggtitle("breakDown plot (direction=down) for randomForest model")

explain_2 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "down", keep_distributions = TRUE)
plot(explain_2, plot_distributions = TRUE) +
         ggtitle("breakDown distributions (direction=down) for randomForest model")

explain_3 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "up", keep_distributions = TRUE)
plot(explain_3, plot_distributions = TRUE) +
         ggtitle("breakDown distributions (direction=up) for randomForest model")

model <- lm(quality~., data=wine)
new_observation <- wine[1,]
br <- broken(model, new_observation)
plot(br)
plot(br, top_features = 2)
plot(br, top_features = 2, min_delta = 0.01)

Arguments

Value

Examples

Contents