If you are interested in solving AI problems and would like an easy to use desktop software that yields state of the art results, you might like TuringBot. In this article, we will show you how it can be used to easily solve classification and regression problems, and explain the methodology that it uses, which is called symbolic regression.
TuringBot is a desktop application that runs on both Windows and Linux, and that can be downloaded for free from the official website. This is what its interface looks like:
The usage is simple: you load your data in CSV or TXT format through the interface, select which column should be predicted and which columns should be used as input, and start the search. The program will look for explicit mathematical formulas that predict this target variable, and show the results in the Solutions box.
The name of this technique, which looks for explicit formulas that solve AI problems, is symbolic regression. It is capable of solving the same problems as neural networks, but in an explicit way that does not involve black box computations.
Think of what Kepler did when he extracted his laws of planetary motion from observations. He looked for algebraic equations that could explain this data, and found timeless patterns that are taught to this day in schools. What TuringBot does is something similar to that, but millions of times faster than a human could ever do.
An important point in symbolic regression is that it is not sufficient for a model to be accurate — it also has to be simple. This is why TuringBot’s algorithm tries to find the best formulas of all possible sizes simultaneously, discarding larger formulas that do not perform better than simpler alternatives.
The problems that it can solve
Some examples of problems that can be solved by the program are the following:
- Regression problems, in which a continuous target variable should be predicted. See here a tutorial in which we use the program to recover a mathematical formula without previous knowledge of what that formula was.
- Classification problems, in which the goal is to classify inputs into two or more different categories. The rationale of solving this kind of problem using symbolic regression is to represent different categorical variables as different integer numbers, and run the optimization with “classification accuracy” as the search metric (this can easily be selected through the interface). In this article, we teach how to use the program to classify the Iris dataset.
- Classification of rare events, in which a classification task must be solved on highly imbalanced datasets. The logic is similar to that of a regular classification problem, but in this case a special metric called F1 score should be used (also available in TuringBot). In this article, we found a formula that successfully classified credit card frauds on a real-world dataset that is highly imbalanced.
If you liked the concept of TuringBot, you can download it for free from the official website. There you can also find the official documentation, with more information about the search metrics that are available, the input file formats and the various features that the program offers.