Apache Spark Tutorial: Machine Learning

The RMSE measures how much error there is between two datasets comparing a predicted value and an observed or known value. The smaller an RMSE value, the closer predicted and observed values are. The R2 ("R squared") or the coefficient of determination is a measure that shows how close the data are to the fitted regression line. This score will always be between 0 and a 100% (or 0 to 1 in this case), where 0% indicates that the model explains none of the variability of the response data around its mean, and 100% indicates the opposite: it explains all the variability. That means that, in general, the higher the R-squared, the better the model fits your data. You'll get back the following result: There's definitely some improvements needed to your model! If you want to continue with this model, you can play around with the parameters that you passed to your model, the variables that you included in your original DataFrame, .... But this is where the tutorial ends for now!