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

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

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

Read MoreSymbolic 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, …).

Read MoreIntroduced in 2009, the Eureqa software gained great popularity with the promise that it could potentially be used to derive new physical laws from empirical data in an automatic way. Details of this reasoning can be found in the original paper, called Distilling Free-Form Natural Laws from Experimental Data.

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

Read MoreRegression 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.

Read MoreGiven a sequence of numbers, finding an explicit mathematical formula that computes the nth term of the sequence can be challenging, except in very special cases like arithmetic and geometric sequences.

Read MoreData science is becoming more and more widespread, pushed by companies that are finding that very valuable and actionable information can be extracted from their databases.

It can be challenging to develop useful models from raw data. Here we will introduce a tool that makes it very easy to develop state of the art models from any dataset.

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