# Why my regression model always be dominanted by one feature?

I am working on a financial predict problem. which means it is a time series prediction problem.

I have three features, which have high correlation(each two's corr is about 0.6) And I do the linear regression fit.

I assume that the coefficient should be similiar among these three features, but i get a coefficient vector like this:

[0.01, 0.15, 0.01]

which means the second features have the biggest coff(features are normalized), and it can dominant the prediction result.

I dont know why. I think adding weak features can boost my prediction model, but i think the second feature is dominant in my model, and other features are worthless.

Why one of features can be dominant in the model, did I miss something?

• What is the scale of the $x$? Standard deviations or "real" units? Did you scale the data? – Peter Apr 4 at 11:12
• @Peter yes, i scale the data by standardize method: x = (x-x.mean())/x.std() – nick Apr 4 at 14:06
• correlation(each two's corr is about 0.6) ? Do you mean -Is it for each of three correlations ? – Subhash C. Davar Apr 6 at 15:01
• Is there regularization being used? What tool/API/parameters are you using? – Craig Apr 7 at 10:44