Break Down Plot
Break Down Plot
## S3 method for class '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 )
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 |
function that is to used for rounding numbers.
It may be |
plot_distributions |
if TRUE then distributions of conditional propotions will be plotted. This requires keep_distributions=TRUE in the broken.default(). |
a ggplot2 object
## Not run:
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])
explain_1 <- broken(model, HR_data[11,-7], data = HR_data[,-7],
predict.function = predict.function, direction = "down")
explain_1
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.