View source: R/accumulated_dependence.R

accumulated_dependence | R Documentation |

Accumulated Local Effects Profiles accumulate local changes in Ceteris Paribus Profiles.
Function `accumulated_dependence`

calls `ceteris_paribus`

and then `aggregate_profiles`

.

accumulated_dependence(x, ...) ## S3 method for class 'explainer' accumulated_dependence( x, variables = NULL, N = 500, variable_splits = NULL, grid_points = 101, ..., variable_type = "numerical" ) ## Default S3 method: accumulated_dependence( x, data, predict_function = predict, label = class(x)[1], variables = NULL, N = 500, variable_splits = NULL, grid_points = 101, ..., variable_type = "numerical" ) ## S3 method for class 'ceteris_paribus_explainer' accumulated_dependence(x, ..., variables = NULL) accumulated_dependency(x, ...)

`x` |
an explainer created with function |

`...` |
other parameters |

`variables` |
names of variables for which profiles shall be calculated.
Will be passed to |

`N` |
number of observations used for calculation of partial dependence profiles.
By default, |

`variable_splits` |
named list of splits for variables, in most cases created with |

`grid_points` |
number of points for profile. Will be passed to |

`variable_type` |
a character. If |

`data` |
validation dataset Will be extracted from |

`predict_function` |
predict function Will be extracted from |

`label` |
name of the model. By default it's extracted from the |

Find more detailes in the Accumulated Local Dependence Chapter.

an object of the class `aggregated_profiles_explainer`

ALEPlot: Accumulated Local Effects (ALE) Plots and Partial Dependence (PD) Plots https://cran.r-project.org/package=ALEPlot, Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

library("DALEX") library("ingredients") model_titanic_glm <- glm(survived ~ gender + age + fare, data = titanic_imputed, family = "binomial") explain_titanic_glm <- explain(model_titanic_glm, data = titanic_imputed[,-8], y = titanic_imputed[,8], verbose = FALSE) adp_glm <- accumulated_dependence(explain_titanic_glm, N = 25, variables = c("age", "fare")) head(adp_glm) plot(adp_glm) library("ranger") model_titanic_rf <- ranger(survived ~., data = titanic_imputed, probability = TRUE) explain_titanic_rf <- explain(model_titanic_rf, data = titanic_imputed[,-8], y = titanic_imputed[,8], label = "ranger forest", verbose = FALSE) adp_rf <- accumulated_dependence(explain_titanic_rf, N = 200, variable_type = "numerical") plot(adp_rf) adp_rf <- accumulated_dependence(explain_titanic_rf, N = 200, variable_type = "categorical") plotD3(adp_rf, label_margin = 80, scale_plot = TRUE)

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