Multiple Linear Regression RMSE vs. number of features/predictors

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Aalaas
Aalaas 2015-10-27
Hello everybody,
I am trying to evaluate the performance of my MLR regression model in terms of the number of input features (or predictors). I have first computed the p-values of each predictor for the result, and then, I have sorted the predictors in such a way that their p-values are in an increasing order. Later, I created N regression models (where N=number of predictors), each one of them adding a new predictor to the model. I computed the RMSE results for the models, and ploted them like: number of predictors used vs. RMSE for each model. I hoped to have a nice curve with a decreasing RMSE as the number of features increased, but not at all. In fact, the RMSE seems to be totally random.
Is there a simple way to obtain such a curve? Or is it necessary to use algorithms such as the Sequential Feature Seection for this purpose?
All your ideas are welcome! Thank you very much!

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