0
$\begingroup$

I am just looking for tips on where to start as I am completely lost with this. This is for a college project were I have been given a data set with 1 response variable(Y) and 2 predictor variables(X1,X2) with 1000 data points. I was told to try and create other variables such exp(X1), X1^2,etc to try and fit the model. However no matter how many polynomial,trigonometric, exponential or other functions I try and fit to the data I can't get the R^2 above 2%. I am not sure if it helps but all the data point Y,X1,X2 range from around(-4,+4). I have included a Matrix scatter plot of the data below.

enter image description here

Any help would be appreciated.

edit: Just to clarify for the project I was told I needed to use a Linear Regression model, Thank you for the reply pvlkmrv,sorry I was unclear that was my fault also I have never used python so even if I was allowed to use A non-linear model I would be unable to use skikit.

Also some new info is I have tried using a Taylors series expansion with all terms up to the 11th power for it and the model still only has an R^2 of 22%(And thats with the 133 polynomial terms) which can be brought down to an R^2 of 11% with stepwise regression(This only has 19 terms) and for both these models the regression p value is 0.000. However once I remove one outlier(It has a y value of -9,I took this value out for the initial Matrix plot shown above) the R^2 goes down to 14% for the 133 term model which has a regression p value of 0.222 and using stepwise regression I get a 31 term model with an R^2 of 4% with a regression p value of 0.12. I presume this means I should get rid of this data point? Also when I remove the other outliers gotten from the boxplot of X1 and X2 the R^2 gets even worse as does the regression p value which leaves me kind of lost.

$\endgroup$
0
$\begingroup$

If the data was generated by a linear combination of nonlinear functions of the input data and you have no clue what those nonlinear functions are, then the solution is to not use a linear model.

Try a multi-layer perceptron (built in to scikit learn and easy to use) or projection pursuit regression (not built in to scikit learn).

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.