Can we derive formulas from neural networks or decision trees?

(updated on June 10, 2020)

I was wondering if we could derive numerical relationships between input variables and output variables from nonlinear model structures such neural networks and decision trees. I find a Q&A on ResearchGate, and I like the answer.

One answer wrote: "It is possible to obtain an equation after developing an ANN for prediction. in fact, you will end up with a long equation including the inputs, weights, biases, etc."

Multi-output Regression Models

(updated on June 10, 2020)

A multi-output regression task predicts multiple numerical properties for each sample (reference).

The article titled "Regression Models with multiple target variables" by Kiran Karkera (link) covers exactly what I am interested. Here are the key points other than the modeling details.

  1. Terminology: multi-output regression or multi-target regression; related terms for classification tasks are multi-label classification, multi-class classification, and multioutput-multiclass classification (aka multi-task classification).
  2. Popular open source ML libraries have little support for the multi-output regression task.

These are the two papers that are mentioned in Kiran Karkera's article.

Here is an article discussing how to develop multi-output regression models with python posted online on March 27, 2020.