I recently read an approach which is used to find the effect of changing an independent variable.
They are doing a classification problem, so each data row (or record) is associated with an outcome of YES or NO.
They take one data row (i.e., test row), and then build a model using the other n-1 rows (more specifically, they use k nearest neighbour to build the model). They use the model to compute the probability of the outcome of that one particular test row. They get a value of, for example, 51% chance that the outcome for this data row is YES.
They then modify the value of one of the independent variable in that test row by X, and the use the model to examine the outcome again. They get a value of 55%, for example.
After the steps, they say that changing the independent variable by X can increase the probability of this test row being YES by 4% (55-51).
Since I am new to the field of statistical analysis and data mining...I am not sure whether this analysis approach is sound. I tried to google for other references for this approach, but I could't find any...
Could someone please help me explain whether this technique is valid or point me to some references? Thank you very much in advance!