DriveML (version 0.1.0)

autoPDP: Generate partial dependence plots

Description

Partial dependence plots (PDPs) help you to visualize the relationship between a subset of the features and the response while accounting for the average effect of the other predictors in the model. They are particularly effective with black box models like random forests and support vector machines.

Usage

autoPDP(train, trainedModel, target, feature, sample = 0.5, modelname,
  seed = 1991)

Arguments

train

[data.frame | Required] Training sample used to train ML model

trainedModel

[model object | Required] The object holding the machine learning model and the data

target

[character | Optional] Target variable name. Specify target variable if model object is other than MLR or driveML

feature

[character | Optional] The feature name for which to compute the effects

sample

[numeric | Optional] Percentage of sample to be considered for training set for faster computation. Default of 0.5

modelname

[character | Optional] specify whcih model to be plotted

seed

[integer | Optional] Random seed number. Default 121

Value

List object containing a plot for each feature listed.

See Also

FeatureEffects plotPartialDependence partial

Examples

# NOT RUN {
# }
# NOT RUN {
#' ## Example using DriveML model object
mymodel = heart.model
pdp_chol = autoPDP(heart, mymodel, feature = "chol", sample = 0.8, seed = 1234)

# Type one MLR package
mod <- mlr::train(makeLearner("classif.ranger"), iris.task)
cc = autoPDP(iris, mod, feature = c("Sepal.Length","Sepal.Width","Petal.Length",
                                   "Petal.Width"), sample = 1, seed = 121)
# Type 2 DrvieML object
hearML <- autoMLmodel(heart,  target = "target_var",  testSplit = 0.2,
tuneIters = 10,  tuneType = "random",
models = "all", varImp = 20,  liftGroup = 50, positive = 1, seed = 1991)
cc = autoPDP(heart, hearML, feature = "chol", sample = 0.8, seed = 1234)

cc1 = autoPDP(heart, trainedModel,target = "target_var", feature = "chol",
sample = 1, modelname = "logreg", seed = 121)

# Type 3 other ML object
library(randomForest)
library(MASS)
rf = randomForest(medv ~ ., data = Boston, ntree = 50)
cc = autoPDP(Boston, rf,target = "medv", feature = "nox", sample = 1, seed = 121)
# }