plotD3.shap | R Documentation |
Plots Shapley values.
## S3 method for class 'shap' plotD3( x, ..., baseline = NA, max_features = 10, digits = 3, rounding_function = round, bar_width = 12, margin = 0.2, scale_height = FALSE, min_max = NA, vcolors = NA, chart_title = NA, time = 0, max_vars = NULL, reload = FALSE )
x |
an explanation created with |
... |
other parameters. |
baseline |
if numeric then veritical line will start in |
max_features |
maximal number of features to be included in the plot. By default it's |
digits |
number of decimal places ( |
rounding_function |
a function to be used for rounding numbers.
This should be |
bar_width |
width of bars in px. By default it's 12px |
margin |
extend x axis domain range to adjust the plot. Usually value between 0.1 and 0.3, by default it's 0.2 |
scale_height |
if |
min_max |
a range of OX axis. By deafult |
vcolors |
If |
chart_title |
a character. Set custom title |
time |
in ms. Set the animation length |
max_vars |
alias for the |
reload |
Reload the plot on resize. By default it's |
a r2d3
object.
Explanatory Model Analysis. Explore, Explain and Examine Predictive Models. https://ema.drwhy.ai
library("DALEX") library("iBreakDown") set.seed(1313) model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_imputed, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_imputed, y = titanic_imputed$survived, label = "glm") s_glm <- shap(explain_titanic_glm, titanic_imputed[1, ]) s_glm plotD3(s_glm) ## Not run: ## Not run: library("randomForest") HR_small <- HR[2:500,] m_rf <- randomForest(status ~. , data = HR_small) new_observation <- HR_test[1,] new_observation p_fun <- function(object, newdata){predict(object, newdata=newdata, type = "prob")} s_rf <- shap(m_rf, data = HR_small[,-6], new_observation = new_observation, predict_function = p_fun) plotD3(s_rf, time = 500) ## End(Not run)
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