I'm conducting an experiment in which I want to compare the performances of 2 models. Both trained using the same algorithm (Logistic regression).

I split the data (n=10000) I have into 3 parts, train1 (n1=5000) and train2 (n2=3000) and test (n3=2000).

I explicitly made sure no observation from both training sets falls into the testing set.

I built 2 models m1 using train1 and m2 using train2. And tested them on the testing set.

I repeated this 100 times. I always find that the model with larger training observations has the highest AIC which is somehow counter-intuitive for me.

Any explication to why this might occur? Does training size affect the AIC?

I'm conducting an experiment in which I want to compare the performances of 2 models. Both trained using the same algorithm (Logistic regression).

I split the data ($n=10000$) I have into 3 parts, train1 ($n_1=5000$) and train2 ($n_2=3000$) and test ($n_3=2000$).

I explicitly made sure no observation from both training sets falls into the testing set.

I built 2 models $m_1$ using train1 and $m_2$ using train2. And tested them on the testing set.

I repeated this 100 times. I always find that the model with larger training observations has the highest AIC which is somehow counter-intuitive for me.

Any explication to why this might occur? Does training size affect the AIC?