Why Multiple Linear Regression performs much better than Neural Network?
I used two methods to try to solve this: neural network (NN) and multi-linear regression (MLR). The prediction results using MLR massively outperform NN. I tried almost everything with the NN, such as grid search to find the optimal "settings", and a lot of trial and error, but still couldn't get the model to perform.
(blue lines are predictions and orange lines are the targets), a perfect model would fit the two lines perfectly.
Results using MLR:
Results Using NN:
I'm not really looking for an answer to fix the NN, but more of an explanation of why this might happen.
Look into overfitting or bias/variance tradeoff. Generally, more complex models require a lot more data to train on. If you have too few data points then the model will memorize the data set rather than learn the pattern in there
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