Playing with Random Forest Classifier, I am wondering what could cause in a 80:20 split the test results to perform better than in a 90:10 split?
With 2000+ data points and:
- with 80:20 split, considering only the test set, the model generates 150 signals with around 55% accuracy
- with 90:10 split, considering only the test set, the model generates 77 signals with around 49% accuracy
From the images, it seems like the more the model 'sees', the worse it gets.