R: Extract Most "Important" Predictors (Experimental)

topPredictors {pdp} | R Documentation

## Extract Most "Important" Predictors (Experimental)

### Description

Extract the most "important" predictors for regression and classification models.

### Usage

    topPredictors(object, n = 1L, ...)

    ## Default S3 method:
    topPredictors(object, n = 1L, ...)

    ## S3 method for class 'train'
    topPredictors(object, n = 1L, ...)

### Arguments

`object` | A fitted model object of appropriate class (e.g., `"gbm"`, `"lm"`, `"randomForest"`, etc.).
`n` | Integer specifying the number of predictors to return. Default is `1` meaning return the single most important predictor.
`...` | Additional optional arguments to be passed onto `varImp`.

### Details

This function uses the generic function `varImp` to calculate variable importance scores for each predictor. After that, they are sorted at the names of the `n` highest scoring predictors are returned.

### Examples

    ## Not run:
    #
    # Regression example (requires randomForest package to run)
    #

    Load required packages
    library(ggplot2)
    library(randomForest)

    # Fit a random forest to the mtcars dataset
    data(mtcars, package = "datasets")
    set.seed(101)
    mtcars.rf <- randomForest(mpg ~ ., data = mtcars, mtry = 5, importance = TRUE)

    # Topfour predictors
    top4 <- topPredictors(mtcars.rf, n = 4)

    # Construct partial dependence functions for top four predictors
    pd <- NULL
    for (i in top4) {
      tmp <- partial(mtcars.rf, pred.var = i)
      names(tmp) <- c("x", "y")
      pd <- rbind(pd,  cbind(tmp, predictor = i))
    }

    # Display partial dependence functions
    ggplot(pd, aes(x, y)) +
      geom_line() +
      facet_wrap(~ predictor, scales = "free") +
      theme_bw() +
      ylab("mpg")

    ## End(Not run)

[Package pdp version 0.7.0 Index]