Estimate how the learned prediction function is affected by one or more features. For a learned function f(x) where x is partitioned into x_s and x_c, the partial dependence of f on x_s can be summarized by averaging over x_c and setting x_s to a range of values of interest, estimating E_(x_c)(f(x_s, x_c)). The conditional expectation of f at observation i is estimated similarly. Additionally, partial derivatives of the marginalized function w.r.t. the features can be computed.

generatePartialDependenceData( obj, input, features = NULL, interaction = FALSE, derivative = FALSE, individual = FALSE, fun = mean, bounds = c(qnorm(0.025), qnorm(0.975)), uniform = TRUE, n = c(10, NA), ... )

obj | (WrappedModel) |
---|---|

input | (data.frame | Task) |

features | character |

interaction | ( |

derivative | ( |

individual | ( |

fun |
A function which operates on the output on the predictions made on the |

bounds | ( |

uniform | ( |

n | ( |

... | additional arguments to be passed to mmpf::marginalPrediction. |

PartialDependenceData. A named list, which contains the partial dependence, input data, target, features, task description, and other arguments controlling the type of partial dependences made.

Object members:

data.frame

Has columns for the prediction: one column for regression and
survival analysis, and a column for class and the predicted probability for classification as well
as a a column for each element of `features`

. If `individual = TRUE`

then there is an
additional column `idx`

which gives the index of the `data`

that each prediction corresponds to.

TaskDesc

Task description.

Target feature for regression, target feature levels for classification, survival and event indicator for survival.

character

Features argument input.

(`logical(1)`

)

Whether or not the features were interacted (i.e. conditioning).

(`logical(1)`

)

Whether or not the partial derivative was estimated.

(`logical(1)`

)

Whether the partial dependences were aggregated or the individual curves are retained.

Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. “Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.” Journal of Computational and Graphical Statistics. Vol. 24, No. 1 (2015): 44-65.

Friedman, Jerome. “Greedy Function Approximation: A Gradient Boosting Machine.” The Annals of Statistics. Vol. 29. No. 5 (2001): 1189-1232.

Other partial_dependence:
`plotPartialDependence()`

Other generate_plot_data:
`generateCalibrationData()`

,
`generateCritDifferencesData()`

,
`generateFeatureImportanceData()`

,
`generateFilterValuesData()`

,
`generateLearningCurveData()`

,
`generateThreshVsPerfData()`

,
`plotFilterValues()`

lrn = makeLearner("regr.svm") fit = train(lrn, bh.task) pd = generatePartialDependenceData(fit, bh.task, "lstat")#>lrn = makeLearner("classif.rpart", predict.type = "prob") fit = train(lrn, iris.task) pd = generatePartialDependenceData(fit, iris.task, "Petal.Width") plotPartialDependence(pd, data = getTaskData(iris.task))