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#SVM HYPERPLAN CODE#
The code for generating the simulated data sets and figures in this chapter are available on the book website. To illustrate the basic concepts of fitting SVMs we’ll use a mix of simulated data sets as well as the employee attrition data. library(vip) # for variable importance plots In this chapter, we’ll explicitly load the following packages: # Helper packages library(dplyr) # for data wrangling library(ggplot2) # for awesome graphics library(rsample) # for data splitting # Modeling packages library(caret) # for classification and regression training library(kernlab) # for fitting SVMs # Model interpretability packages library(pdp) # for partial dependence plots, etc. We’ll also use caret for tuning SVMs and pre-processing. 2019) and svmpath (Hastie 2016)), we’ll focus on the most flexible implementation of SVMs in R: kernlab (Karatzoglou et al. 22.2 Measuring probability and uncertaintyĪlthough there are a number of great packages that implement SVMs (e.g., e1071 (Meyer et al.21.3.2 Divisive hierarchical clustering.21.3.1 Agglomerative hierarchical clustering.21.2 Hierarchical clustering algorithms.18.4.2 Tuning to optimize for unseen data.17.5.2 Proportion of variance explained criterion.17.5 Selecting the number of principal components.
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16.8.3 XGBoost and built-in Shapley values.16.7 Local interpretable model-agnostic explanations.16.5 Individual conditional expectation.16.3 Permutation-based feature importance.16.2.3 Model-specific vs. model-agnostic.7.2.1 Multivariate adaptive regression splines.7 Multivariate Adaptive Regression Splines.