This tutorial covers the following points:

• 00:11 – fit a line to your data
• 00:21 – fit a polynomial
• 00:41 – using base formulas and constants
• 00:52 – choosing input variables for each term
• 01:05 – use all variables as input except for one
• 01:23 – custom left side of equation
• 01:42 – classification problems
• 02:02 – calculating an average
• 02:13 – command line usage
• 02:38 – fine-tuning a function

What is Symbolic Regression and How Does it Work?

Symbolic Regression is a great method for discovering hidden relationships between variables. It accomplishes this task by turning data into explicit mathematical formulas.

Python: Symbolic Regression in 3 Easy Steps

Looking for a Symbolic Regression library for Python that will allow you to turn your data into nice mathematical formulas? TuringBot is by far the easiest to use. Here we will show how to use it.

TuringBot 1.9 released!

Today we have released a new version of TuringBot with two much-awaited features: custom searches and history functions.

Symbolic Regression in Python with TuringBot

In this tutorial, we are going to show a very easy way to do symbolic regression in Python.

10 creative applications of symbolic regression

Symbolic regression is a method that discovers mathematical formulas from data without assumptions on what those formulas should look like. Given a set of input variables x1, x2, x3, etc, and a target variable y, it will use trial and error find f such that y = f(x1, x2, x3, …).

Decision boundary discovery with symbolic regression

An interesting classification problem is trying to find a decision boundary that separates two categories of points. For instance, consider the following cloud of points: