# Generating a new independent variable from other independent variables in the dataset

I have a database of characteristics of a signal. The characteristics are a bunch of indicators (kurtosis,skewness,etc). The dependent variable is fault. The fault variable has as a value 1 if the signal corresponds to a faulty machine and 0 if it's not a faulty machine.

I would like to know if there is a method or an algorithm capable of generating indicators (new independent variable) from the ones in the database(e.g :the multiplication of 2 of them or division ) that would correlate better with the dependent variable and would consequently a better indicator of the existence of the fault.

Thank you !

NB : If technical terms are not well used or more details are needed, don't hesitate to ask me as I am new to the field.

You want to look at variable interactions. When building your model, consider something more of the form $y = (x_1 + x_2 + \dots +x_n)^2$, which will give you the pairwise interaction terms (higher exponents will reveal interactions between more variables). You can then see if these have low p-values, etc.