Regression - predict n numbers based on another n numbers

I'd like to get a recommendation how to attack a problem of predicting multiple numbers. Training data contains 4 columns, each says a probability of record being in the bucket. So, for example: X = [0.25, 0.5, 0.25, 0.0] and corresponding output should be e.g. Y = [0.8, 0.1, 0.0, 0.1]. Each row should sum to 1.0

What type of approach would you recommend?

I already tried simple neural network with 4 neurons in the last layer and softmax activation but wondering if there is a better solution.

Thanks!

• What's Y? softmax tends to give a low variance probabilty Commented May 28, 2018 at 12:10
• let's say I have a system that estimates a age distribution in a group of people. X says their age distribution. For example x[0] is 0-18 years, x[1] = 19-40 years, x[2] = 41-60 years and x[3] is 61+ years. x[0] = 0.25 means 25% of people belong to the age group 0-18 years. And Y contains real distribution. Commented May 28, 2018 at 12:19
• Still confused. When creating the dataset, how did you create X and Y? Also, how will you use the predictor afterwards? Commented May 28, 2018 at 13:00
• You can have an image of people and would like to estimate their ages. There is a system that generates these estimates (X) but I know it's not entirely correct (because I have several thousands of manually labelled images) and I'd like to build a system that reduce the error. HTH Commented May 28, 2018 at 13:05