View source: R/predict_diagnostics.R

predict_diagnostics | R Documentation |

This function performs local diagnostic of residuals. For a single instance its neighbors are identified in the validation data. Residuals are calculated for neighbors and plotted against residuals for all data. Find information how to use this function here: https://ema.drwhy.ai/localDiagnostics.html.

predict_diagnostics( explainer, new_observation, variables = NULL, ..., nbins = 20, neighbors = 50, distance = gower::gower_dist ) individual_diagnostics( explainer, new_observation, variables = NULL, ..., nbins = 20, neighbors = 50, distance = gower::gower_dist )

`explainer` |
a model to be explained, preprocessed by the 'explain' function |

`new_observation` |
a new observation for which predictions need to be explained |

`variables` |
character - name of variables to be explained |

`...` |
other parameters |

`nbins` |
number of bins for the histogram. By default 20 |

`neighbors` |
number of neighbors for histogram. By default 50. |

`distance` |
the distance function, by default the |

An object of the class 'predict_diagnostics'. It's a data frame with calculated distribution of residuals.

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

library("ranger") titanic_glm_model <- ranger(survived ~ gender + age + class + fare + sibsp + parch, data = titanic_imputed) explainer_glm <- explain(titanic_glm_model, data = titanic_imputed, y = titanic_imputed$survived) johny_d <- titanic_imputed[24, c("gender", "age", "class", "fare", "sibsp", "parch")] id_johny <- predict_diagnostics(explainer_glm, johny_d, variables = NULL) id_johny plot(id_johny) id_johny <- predict_diagnostics(explainer_glm, johny_d, neighbors = 10, variables = c("age", "fare")) id_johny plot(id_johny) id_johny <- predict_diagnostics(explainer_glm, johny_d, neighbors = 10, variables = c("class", "gender")) id_johny plot(id_johny)

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