# How can I ensure about my R^2 score?

I have a dataset with 10 columns and 158 rows. I try to predict my test dataset which is 1 columns with 158 rows.

I made cross-validations, grid-search and use ElasticNet algorithm.

Also before the evaluate the model I check the pearson correlation between 10 columns which I used for train with other 1 column which I try to predict. The correlation is not good but when I evaluate model the R^2 score is near 0.98 .

How can I be ensure that this score is confidental ? Because I didn't expect a R^2 like this. This is too high that I expect.

$R^2$ shows what variation of your purpose variable is described by independent variables. So their synergetic effect could give you good better answer than their correlation. Better use $R^2 adjusted$. Look at p-values of your variables and think about their real correlation. Are they important in real life. If they have any adequate relationship, so your regression is right.