I found out something weird. Perhaps this is due to my lack of comprehension. But please hear me.

After some model screening with the train split of the data set and chosen a model to work with. At least there are two ways to continue right?

Let's say there's a data called Z, it was split into X for train, and Y for test.

Model screening happened in X.

- First is by applying the last_fit() function to the original data Z before it was split, then collect the metrics
- Or, by applying fit to train data X with the best model, then apply predict to test data Y.

I found out that the two approach resulted in far different results. With last_fit, it's 70 something percent, while using fit to train, then predict to test, resulted in 20 something percent.

What's this? Why?