# Can a neural network regression improve the R² value drastically from other regression techniques?

I am doing a multivariate regression analysis with 15 input features, 1 output feature with 1600 samples. I tried SVR, Random forest regressor, KNN, linear, poly and regularized regressions. After trying all, I end up in getting a R² value not more than 0.60.

1. If I use neural net, is there a possibility to improve the R² value up to 0.90? (cz, I dunno on what logic does ANN work and I read we can do any thing with ANN)

2. Any suggestions to get a better R² value ?

3. Is 0.60 is a better R² value? (of course it depends on application and problem type, but I like to know in general)

• I don't think there is such a thing as a "good" $R^2$ or a "bad" $R^2$. An acceptable value of this statistic is completely dependent on the data and the problem being solved. There's no absolute meaning for these statistics, they are comparative in nature. – Matthew Drury Oct 19 '17 at 1:21