I have a small table like this
Outcome Feature_1_Value Feature_2_Value Feature_3_Value ..... 1 1 1.2 3.7 9.7 2.3 3.976 4.65 .... .... .... ....
There is an outcome that depends on what values do a set of features have. The above table represents the training data. The outcomes and the features all are continuous values and are not discrete ( it is not 0/1/2 kind of values).
Now, how do I analyze the training set of data to determine which of the features actually influence the output and how do I re-arrange the features in decreasing order of their influence? I would like to represent nicely to the end-user saying Feature_n_Value was twice more influencing the output than Feature_m_Value, but Feature_n_value was less than half influential for the output compared to Feature_p_Value, and so on.
What is the mathematics behind such a calculation. I looked a bit at logistic regression. Would that be a right way to do? If so, then in logistic regression, I get a set of beta vectors (num of output values), each of the size of the dimension of the input (num of features). What do I do with the output beta vectors, though I know that you can predict an output given a fresh set of feature values and these beta values that are calculated using training data.
Advice is highly appreciated.
Thanks Abhishek S