I am using sklearn's Linear Regression ML model in Python to predict. The predict function returns an array with a lot of floating point numbers, (which is correct) but I don't quite understand what the floating point numbers represent. Is it possible to map them back?
For context, I am trying to predict sales of a product (label) from stocks available. The predict function returns a large array of floating point numbers. How do I know what each floating point number represents?
For instance, the array is like [11.5, 12.0, 6.1,..]. It seems 6.1 is the sales qty but with what stock quantity is it associated with?
i'th output is prediction for i'th input. Whatever you passed to .predict is a collection of objects, and the ordering of predictions is the same as the ordering of data passed in.