Different accuracy of random forest and neural net I ran a set of time series data in  a neural net model and random forest model. For neural net I normalised the data split it into test 20%, validation and training 80%. Keras library neural net was used here.The accuracy is 85%(I get different accuracy every time).
For random forest I didn’t normalise the data and the test train split is 20% and 80%.I used scikit learn here. I am getting 93% accuracy .Here time series data is used as well. Am I not supposed to get similar accuracy as neural net? What am I doing wrong?
(I was reading some papers there they had similar kind of accuracy. That’s why I am asking. Also can random forest overfit? Is mine one overfitting?!
Different models will give different accuracies. Same model too could give you different accuracies. You can try setting the seeds to predefined values to bring in more consistency in the same model results