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

autoPDP

R Documentation

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
)

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

Value

List object containing a plot for each feature listed.

Examples



#' ## 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)

DriveML documentation built on Dec. 2, 2022, 5:14 p.m.