Solved – Mcnemar’s test p-value output in R confusion matrix

I am using the R caret package to build a randomforest classifier model for plant data.

The dataset has 7 variables – all numeric which are being used to predict if a plant will "grow" or "not grow".

This is a very simple model.

In my training dataset I have 70% of observations classified as "grow" and 30% classified at "not grow".

I have trained the model using this data and have received an accuracy of 93% and a kappa of 86%.

My question is about the Mcnemar's Test:
I have a p-value of 0.8231 which I understand usually means I reject the Null hypothesis.

I have attempted to research this test and it appears to be something about proportion change before and after an event.

Would I be correct in saying this has something to do with my imbalanced proportion in my dependent variable?

Could anyone interpret this p-value?

Thank you

First, a p-value of 0.83 means you do not reject the null.

Second, McNemar's test is about whether the row and column marginals are equal, or, equivalently, whether the "off-diagonal" elements are equal. Since your p value is quite high, you cannot reject the null that they are equal. It's not clear, from your question, what was in the four cells of the crosstabulation.

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