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make pdp and cp work with NA in data #120
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I've could not find a reproducible example, I've checked this with
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I guess that after the fix it works library("DALEX")
library("ingredients")
library("randomForest")
model_titanic_glm <- randomForest(survived ~ gender + age + fare,
data = na.omit(titanic_imputed))
titanic_imputed[2:1000,2] = NA
explain_titanic_glm <- explain(model_titanic_glm,
data = titanic_imputed[,-8],
y = titanic_imputed[,8],
verbose = FALSE)
pdp_glm <- partial_dependence(explain_titanic_glm,
N = 25, variables = c("age", "fare","sibsp"))
#, variable_splits = list(age = seq(0,100,0.1), fare = c(0:100), sibsp=0:10))
plot(pdp_glm) |
thanks |
Hi there, I'm wondering if there is some way of making conditional and accumulated dependence plots work with NAs? i,e,
Thanks |
Hi, what is your goal? PD/ALE rely on estimating expected predictions with respect to data distribution. Did you consider removing observations without |
Sorry, this was a bad example. I was piggybacking on the example from this thread. In doing more testing with reasonable numbers of NAs, I see that
Unfortunately, in my significantly larger and more complicated models, I'm running into issues related to missing values where the aggregated profiles aren't being calculated. When I impute the missing values, there are no issues. But I can't seem to recreate it with a simpler dataset/model. Do you know of any situations where aggregating profiles fails elated to NAs? There are no instances where an entire column is NAs like in my previous examples. |
crossref ModelOriented/modelStudio#71
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