You see this a lot in the context of building a regression and often times the assumptions are violated:
* Linear relationship between predictor and response and no multicollinearity
* No auto-correlation (statistical independence of the errors)
* Homoscedasticity (constant variance) of the errors
* Normality of the residual(error) distribution.
As the paper suggests, plotting the data visually will help you avoid these assumptions, but also just making sure you don't violate the assumptions w/ statistical tests would work too. For example, uou can look at your residuals (loss) as an indicator of good fit. If your residuals do not follow a normal distribution, this is typically a warning sign that your R2 score is dubious.
There are a few statistical tests for Residual Normality, particularly, the Jaque-Bara test is common and available in scipy.
* Linear relationship between predictor and response and no multicollinearity * No auto-correlation (statistical independence of the errors) * Homoscedasticity (constant variance) of the errors * Normality of the residual(error) distribution.
As the paper suggests, plotting the data visually will help you avoid these assumptions, but also just making sure you don't violate the assumptions w/ statistical tests would work too. For example, uou can look at your residuals (loss) as an indicator of good fit. If your residuals do not follow a normal distribution, this is typically a warning sign that your R2 score is dubious.
There are a few statistical tests for Residual Normality, particularly, the Jaque-Bara test is common and available in scipy.
So, I would argue, you don't even need to visualize the data. I describe this more here: http://www.eggie5.com/104-linear-regression-assumptions