autoPDP: Generate partial dependence plots
In daya6489/DriveML: Self-Drive Machine Learning Projects

Description Usage Arguments Value See Also Examples

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.

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

`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

List object containing a plot for each feature listed.

FeatureEffects plotPartialDependence

#' ## 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)