# My models performs better with the arbitrary random feature. How can I interpret this?

I am training 6 different classifiers 'Decision Tree', 'Random Forest', 'Logistic regression' and 'SVM' with different kernels. There are about 80 dependent variables including categorical and numerical variables. For my experiment, I added a 'random' column which is generated by any arbitrary random numbers, but all the model performs better on both validation set and test set. Is there any good explanation about this phenomenon?

• Hypothesis 1: mistake in the interpretation of the results. Is the performance improvement significant? If not, it might simply be due to chance. That would mean that none of the models actually uses the random feature, one happens to be slightly better by chance. However the chances that this would happen with 6 distinct classifiers are very low ($$1/2^6$$ to be precise)