Hi @amrutas,
The image you've posted looks like it came from a Forest model. Are you trying to extract the variable importance values from a boosted model, random forest, or both? I ask because the process to do so will be slightly different depending on the model.
Either way, at a high level you will want to:
1. Connect an R tool to the O anchor of the tool you used to generate your model. The O stands for Object, and this is where the model object is streamed onto the canvas.
2. Write R code into the tool to read in the model and unserialize the object.
3. Load in the R package that corresponds to your model, this is randomForest for a random forest model and gbm for a boosted model.
4. Apply the corresponding R function to the model to extract the variable importance metric. This is importance() for a random forest and summary() for boosted models.
5. Write the resulting data back out to Alteryx.
The code to do this for a random forest model:
# Read in serialized model object
modelObj <- read.Alteryx("#1", mode="data.frame")
# Unserialize model
model <- unserializeObject(as.character(modelObj$Object[1]))
# load library
library(randomForest)
# extract feature importance
importance <- importance(model)
# convert to data frame
output <- data.frame(var = row.names(importance), importance)
# write out data frame
write.Alteryx(output, 1)
The code to do this for a boosted model:
# Read in serialized model object
modelObj <- read.Alteryx("#1", mode="data.frame")
# Unserialize model
model <- unserializeObject(as.character(modelObj$Object[1]))
# load package
library(gbm)
# extract variable importance
output <- summary(model)
# write out to Alteryx
write.Alteryx(output, 1)
I've attached a workflow that demonstrates the process for both a boosted model and a random forest model. Please be sure to read the documentation on each of the functions used to extract the variable importance metrics (hyperlinked above).
Hope this helps!
Sydney
