It's my first question, I am not so experienced in stats: feel free to point me to the right direction.
I am doing a regression to predict a price. I wanted to check the residuals (the difference between the target and the predicted value) for clues to improve the prediction. I note a linear pattern (see below), I am right in interpreting that I likely miss a linear predictor? What is the best way to fix such issue? Would it make sense to estimate the linear coefficient, and add it to the (linear) model?
The data is from there: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
The data description is : https://ww2.amstat.org/publications/jse/v19n3/Decock/DataDocumentation.txt
The model is the https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor
for which I selected 10 predictors.
In parallel I also made a linear model: https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor which use the same predictors. The residuals also shows a linear pattern (together with the outlier), although the mean seems to be zero.