plot_model_comparison: Plot models comparison

In ModelOriented/xspliner: Assisted Model Building, using Surrogate Black-Box Models to Train Interpretable Spline Based Additive Models

Description

The function plots models comparison based on them predictions.

Usage

plot_model_comparison(x, model, data, compare_with = list(),
  prediction_funs = list(function(object, newdata) predict(object,
  newdata)), sort_by = NULL)

Arguments

x	Object of class 'xspliner'
model	Base model that xspliner is based on.
data	Dataset on which predictions should be compared.
compare_with	Named list. Other models that should be compared with xspliner and model.
prediction_funs	Prediction functions that should be used in model comparison.
sort_by	When comparing models determines according to which model should observations be ordered.

Examples

iris_data <- droplevels(iris[iris$Species != "setosa", ])
library(e1071)
library(randomForest)
library(xspliner)
# Build SVM model, random forest model and surrogate one constructed on top od SVM
model_svm <- svm(Species ~  Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
                 data = iris_data, probability = TRUE)
model_rf <- randomForest(
  Species ~  Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
  data = iris_data
)
model_xs <- xspline(
  Species ~  xs(Sepal.Length) + xs(Sepal.Width) + xs(Petal.Length) + xs(Petal.Width),
  model = model_svm
)
# Prepare prediction functions returning label probability
prob_svm <- function(object, newdata)
  attr(predict(object, newdata = newdata, probability = TRUE), "probabilities")[, 2]
prob_rf <- function(object, newdata)
  predict(object, newdata = newdata, type = "prob")[, 2]
prob_xs <- function(object, newdata)
  predict(object, newdata = newdata, type = "response")

# Plotting predictions for original SVM and surrogate model on training data
plot_model_comparison(
  model_xs, model_svm, data = iris_data,
  prediction_funs = list(xs = prob_xs, svm = prob_svm)
)
# Plotting predictions for original SVM, surrogate model and random forest on training data
plot_model_comparison(
  model_xs, model_svm, data = iris_data,
  compare_with = list(rf = model_rf),
  prediction_funs = list(xs = prob_xs, svm = prob_svm, rf = prob_rf)
)
# Sorting values according to SVM predictions
plot_model_comparison(
  model_xs, model_svm, data = iris_data,
  compare_with = list(rf = model_rf),
  prediction_funs = list(xs = prob_xs, svm = prob_svm, rf = prob_rf),
  sort_by = "svm"
)

ModelOriented/xspliner documentation built on Oct. 5, 2019, 3:42 p.m.