I have used two algorithms(methods) on few datasets and obtained some results. Now I want to check whether the obtained results are comparable or not? I have used two sampled K-S test and got the following result, now how to interpret the test results? and what should be the conclusion?
KstestResult(statistic=0.11320754716981132, pvalue=0.8906908896753045)
D_alpha= 0.2641897545611759
The K-S test is used to compare distributions. So, when you apply it, you are interested in comparing distribution say F(X) with another distribution $G(X)$. Typically, the former is your data distribution and the latter is either another data distribution or something you specify (e.g. Gaussian).
Now, the null hypothesis of the test is that F(X)=G(X). The test produces a statistic to asses whether this null hypothesis can be assumed to be true. You do not typically look directly at this quantity; rather you want to look at the p-value. This is the probability of observing a statistic greater than the one you observed, if the null hypothesis were true. In this case, a low value of the p-value indicates that it is veri unlikely (i.e. low probability) to observe a statistic greater than the one you observed if the hypothesis were true; thus, since you observed a statistic which gives you a small p-value, you tend to believe that your null hypothesis must be false, and that indeed F(X) is different from G(X).
In your case the p-value is high (for low p-value, we intend 0.1, 0.05 or 0.01 or lower), so you do not rejecte the null hypothesis. Thus, you would say that the things that you tested are the same and are not statistically different.
However, I strongly encourage you to read more about the test, how it is used and when; also try to understand if it is appropriate in your case. You can do it on Wikipedia and on the scipy docs