DriveML (version 0.1.5)

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, gradient boosting, etc.

Usage

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

Value

List object containing a plot for each feature listed.

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 is 121

See Also

FeatureEffects plotPartialDependence

Examples

# \donttest{
#' ## Example using DriveML model object
mymodel = heart.model
pdp_chol = autoPDP(heart, mymodel, feature = "chol", sample = 0.8, seed = 1234)

# Type 1 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)

# Type 2 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)
# }