This function computes various (instance and dataset level) model explanations and produces an interactive, customisable dashboard. It consists of multiple panels for plots with their short descriptions. Easily save and share the HTML dashboard with others. Tools for model exploration unite with tools for Exploratory Data Analysis to give a broad overview of the model behavior.
Theoretical introduction to the plots: Explanatory Model Analysis: Explore, Explain and Examine Predictive Models
Displayed variable can be changed by clicking on the bars of plots or with the first dropdown list,
and observation can be changed with the second dropdown list.
The dashboard gathers useful, but not sensitive, information about how it is being used (e.g. computation length,
package version, dashboard dimensions). This is for the development purposes only and can be blocked by setting telemetry to FALSE.
modelStudio(explainer, ...) # S3 method for explainer modelStudio( explainer, new_observation = NULL, new_observation_y = NULL, facet_dim = c(2, 2), time = 500, max_features = 10, N = 300, B = 10, eda = TRUE, show_info = TRUE, parallel = FALSE, options = ms_options(), viewer = "external", widget_id = NULL, telemetry = TRUE, ... )
| explainer | An |
|---|---|
| ... | Other parameters. |
| new_observation | New observations with columns that correspond to variables used in the model. |
| new_observation_y | True label for |
| facet_dim | Dimensions of the grid. Default is |
| time | Time in ms. Set the animation length. Default is |
| max_features | Maximum number of features to be included in BD and SV plots.
Default is |
| N | Number of observations used for the calculation of PD and AD.
|
| B | Number of permutation rounds used for calculation of SV and FI.
Default is |
| eda | Compute EDA plots. Default is |
| show_info | Verbose a progress on the console. Default is |
| parallel | Speed up the computation using |
| options | Customize |
| viewer | Default is |
| widget_id | Use an explicit element ID for the widget (rather than an automatically generated one).
Useful e.g. when using |
| telemetry | The dashboard gathers useful, but not sensitive, information about how it is being used (e.g. computation length,
package version, dashboard dimensions). This is for the development purposes only and can be blocked by setting |
An object of the r2d3, htmlwidget, modelStudio class.
The input object is implemented in DALEX
Feature Importance, Ceteris Paribus, Partial Dependence and Accumulated Dependence plots are implemented in ingredients
Break Down and Shapley Values plots are implemented in iBreakDown
library("DALEX")#> #> #> #>library("modelStudio") #:# ex1 classification on 'titanic' data # fit a model model_titanic <- glm(survived ~., data = titanic_imputed, family = "binomial") # create an explainer for the model explainer_titanic <- explain(model_titanic, data = titanic_imputed, y = titanic_imputed$survived, label = "Titanic GLM")#> Preparation of a new explainer is initiated #> -> model label : Titanic GLM #> -> data : 2207 rows 8 cols #> -> target variable : 2207 values #> -> predict function : yhat.glm will be used ( default ) #> -> predicted values : numerical, min = 0.008128381 , mean = 0.3221568 , max = 0.9731431 #> -> model_info : package stats , ver. 4.0.2 , task classification ( default ) #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -0.9628583 , mean = -2.569729e-10 , max = 0.9663346 #> A new explainer has been created!# pick observations new_observations <- titanic_imputed[1:2,] rownames(new_observations) <- c("Lucas","James") # make a studio for the model modelStudio(explainer_titanic, new_observations) # \donttest{ #:# ex2 regression on 'apartments' data library("ranger") model_apartments <- ranger(m2.price ~. ,data = apartments) explainer_apartments <- explain(model_apartments, data = apartments, y = apartments$m2.price)#> Preparation of a new explainer is initiated #> -> model label : ranger ( default ) #> -> data : 1000 rows 6 cols #> -> target variable : 1000 values #> -> predict function : yhat.ranger will be used ( default ) #> -> predicted values : numerical, min = 1887.17 , mean = 3488.484 , max = 6157.011 #> -> model_info : package ranger , ver. 0.12.1 , task regression ( default ) #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -398.7131 , mean = -1.465429 , max = 614.2234 #> A new explainer has been created!new_apartments <- apartments[1:2,] rownames(new_apartments) <- c("ap1","ap2") # change dashboard dimensions and animation length modelStudio(explainer_apartments, new_apartments, facet_dim = c(2, 3), time = 800) # add information about true labels modelStudio(explainer_apartments, new_apartments, new_observation_y = new_apartments$m2.price) # don't compute EDA plots modelStudio(explainer_apartments, eda = FALSE)#> #>#:# ex3 xgboost model on 'HR' dataset library("xgboost") HR_matrix <- model.matrix(status == "fired" ~ . -1, HR) # fit a model xgb_matrix <- xgb.DMatrix(HR_matrix, label = HR$status == "fired") params <- list(max_depth = 3, objective = "binary:logistic", eval_metric = "auc") model_HR <- xgb.train(params, xgb_matrix, nrounds = 300) # create an explainer for the model explainer_HR <- explain(model_HR, data = HR_matrix, y = HR$status == "fired", label = "xgboost")#> Preparation of a new explainer is initiated #> -> model label : xgboost #> -> data : 7847 rows 6 cols #> -> target variable : 7847 values #> -> predict function : yhat.default will be used ( default ) #> -> predicted values : numerical, min = 1.158531e-06 , mean = 0.36388 , max = 0.9997931 #> -> model_info : package Model of class: xgb.Booster package unrecognized , ver. Unknown , task regression ( default ) #> -> residual function : difference between y and yhat ( default ) #> -> residuals : numerical, min = -0.9830738 , mean = -4.673149e-05 , max = 0.9703595 #> A new explainer has been created!