R squared comparison I have 5 features in my data.
The R squared value when I use features 1,2, and 3 is $x$ and the R squared value when I use features 1,3, and 4 is $x + 0.1.$
Does this mean my second model is better than first model?

SuperUser, here are a couple of examples along the lines of what Galen suggested.
For example one model may be chasing (fitting) the "Noise" better than the other. One could assess Predictive R squared (a form of cross validation (leave one out)), and it is possible that the Higher R squared model has a lower Predictive R squared. R squared is highly dependent on the dataset and may not be representative of the ability to predict.
Here is a general introductory discussion of R squared vs Predictive R squared with some good links:
https://www.datasciencecentral.com/alternatives-to-r-squared-with-pluses-and-minuses
Another example: Let's say the cost of getting the feature "4" is high (difficult to get), but feature "2" is easier (less costly) to obtain. The first model would be "better" even though the R squared is not as high.
