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?!
Think of your model as a function that maps input features to the response variable. Random forest classifier/regressor is always a piece-wise constant function. Neural nets are continuous functions (they are the successive implementation of linear maps and continuous activation functions). They are always different, so your work is accurate here.
For consistency issues, try setting up the seed as mentioned above. Also, try adding cross-validation to your model to avoid too good/too bad random choices.