How to check randomness in a machine learning dataset? given a standard machine learning dataset, is it possible to check whether the relationship between the inputs and outputs is random or not? If the relationship between inputs and outputs is random (or nearly random) then we should not expect much performance from any machine learning algorithm. 
Thanks
Yes, this question is almost the same as mine question. But I'm not sure I understood the answers, so I would like to make my question more specific. Assume two continuous valued variables x and y. Is it possible to show that the relation between x and y is random (or almost random)? For example we can check the correlation between x and y, if there is no correlation then can we say the relationship is random? But is being correlated the opposite of being random? Any ideas?
 A: For pairwise linear relationships between a few predictors and a response variable you can use statistical inference to estimate the probability that each predictor is independently related w/ the response. You can also use model fit measures to quantify the how much the response can be predicted with a model using a set of predictors. The problem is that the more variables you include, the better the fit, but this does not necessarily mean that there is a relationship because you might be overfitting. In my opinion, such check must use cross-validation to quantify generalization to unseen data. It is difficult to tell apart that there is no relationship or whether your model is incorrect (eg, too simple). This would show-up in cross-validation as underfitting, ie, similar train and test errors. Another useful thing is to randomly switch labels and see that the test error behaves the same. But as I said, it is difficult to tell apart from when using an incorrect model
A: The question, as it's written is : "Is it possible?" I would say NO.
Because the whole differents stages of a machine learning algorithm is to find what is the relationship between the two sets of data. At this point, if can explain what kind of relation there is, you can have a model to predict it.
But you can check for some easy correlation. 
