## A Guide to Symbolic Regression Machine Learning

Symbolic Regression is a technique that discovers explicit mathematical formulas that connect variables on a dataset. This allows machine learning problems to be solved in a very elegant and robust way. Here we will talk about this method and its advantages.

## Forecasting the growth of a company with a formula

Say you have a company or want to invest in a company, and you want to predict how much it will grow in the coming months or years. How to do that?

Here we will show how this problem can be modeled in a very simple way using symbolic regression.

## 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:

## How to create an AI trading system

Predicting whether the price of a stock will rise or fall is perhaps one of the most difficult machine learning tasks. Signals must be found on datasets which are dominated by noise, and in a robust way that will not overfit the training data.

## How to create an equation for data points?

In order to find an equation from a list of values, a special technique called symbolic regression must be used. The idea is to search over the space of all possible mathematical formulas for the ones with the greatest accuracy, while trying to keep those formulas as simple as possible.

## Machine learning black box models: some alternatives

In this article, we will discuss a very basic question regarding machine learning: is every model a black box? Certainly most methods seem to be, but as we will see, there are very interesting exceptions to this.

## A regression model example and how to generate it

Regression models are perhaps the most important class of machine learning models. In this tutorial, we will show how to easily generate a regression model from data values.