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library(pdp) # Set global chunk options knitr::opts_chunk$set( cache = TRUE, comment = "#>", error = FALSE, fig.path = "figure/", cache.path = "cache/", dpi = 300, fig.align = "center", fig.asp = 0.618, fig.pos = "!htb", fig.width = 6, fig.show = "hold", message = FALSE, out.width = "100%", par = TRUE, # defined below size = "small", # size = "tiny", tidy = FALSE, warning = FALSE ) # Set general hooks knitr::knit_hooks$set( par = function(before, options, envir) { if (before && options$fig.show != "none") { par( mar = c(4, 4, 0.1, 0.1), cex.lab = 0.95, cex.axis = 0.8, # was 0.9 mgp = c(2, 0.7, 0), tcl = -0.3, las = 1 ) if (is.list(options$par)) { do.call(par, options$par) } } } )
Partial dependence (PD) plots are rather straightforward to construct in practice, as discussed in @RJ-2017-016. However, is is difficult to apply the brute force algorithm as is to situations where the data are stored in a Spark data frame; such is the case when fitting models using Spark's MLlib library [@meng-2015-mllib]. Fortunately, the same computations can be done using a couple of simple Spark operations; in particular, a cross-join, followed by a group-by and aggregation step.
To illustrate...
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