knitr keeps failing

[theokirkland]

I am trying to make an HTML file from a RStudio notebook.

I keep getting the error

The code on line 252 is below

varimp_mars
Any help would be appreciated.

[williaml]

Is there a problem with that line? Without more context it is impossible to help.

[rivaquiroga]

If you are absolute sure that the code works, then it might be a cache issue. Try cleaning the cache in Preview ("Clear knitr cache...") and then knit the notebook again.

[theokirkland]

The code works. I do get figures in html but no notebook.

Tried clearing the cache but that didn’t help.

Error is "Error in Line 348 train.default(x, y …Execution halted.”

I Tried to attach the file but it wasn't allowed.
Here is some of the code

dim(testData)
sum(is.na(testData))
# Step 1: Impute missing values 
preProcess_missingdata_model <- preProcess(testData, method='knnImpute')
testData2 <- predict(preProcess_missingdata_model,testData)  
sum(is.na(testData2))
# Step 2: Create one-hot encodings (dummy variables)
testData3 <- predict(dummies_model, testData2)
is.factor(testData3)
# Step 3: Transform the features to range between 0 and 1
testData4 <- predict(preProcess_range_model, testData3)

# View
head(testData4)

# Predict on testData
predicted2 <- predict(model_mars, testData4)
head(predicted2)

###6.5. Confusion Matrix

###The confusion matrix is a tabular representation to compare the predictions (data) vs the actuals (reference). By setting mode='everything' pretty much most classification evaluation metrics are computed.

# Compute the confusion matrix
confusionMatrix(reference = testData$Purchase, data = predicted2, mode='everything', positive='MM')

set.seed(100)
model_mars2 = train(Purchase ~ ., data=trainData, method='earth', tuneLength = 5, metric='ROC', trControl = fitControl)
model_mars2

# Step 2: Predict on testData and Compute the confusion matrix

confusionMatrix(reference = testData$Purchase, data = predicted2, mode='everything', positive='MM')

line 348 was "set.seed(100) is the last block.

I’m frustrated. This has worked in the past.

Thanks

Theo

[cderv]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Thank you !