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, …).

The method is very general, given that the target variable y can be anything, and given that a variety of error metrics can be chosen for the search. Here we want to enumerate a few creative applications to give the reader some ideas.

All of these problems can be modeled out of the box with the TuringBot symbolic regression software.

1. Forecast the next values of a time series

Say you have a sequence of numbers and you want to predict the next one. This could be the monthly revenue of a company or the daily prices of a stock, for instance.

In special cases, this kind of problem can be solved by simply fitting a line to the data and extrapolating to the next point, a task that can be easily accomplished with numpy.polyfit. While this will work just fine in many cases, it will not be useful if the time series evolves in a nonlinear way.

Symbolic regression offers a more general alternative. One can look for formulas for y = f(index), where y are the values of the series and index = 1, 2, 3, etc. A prediction can then be made by evaluating the resulting formulas at a future index.

This is not a mainstream way to go about this kind of problem, but the simplicity of the resulting models can make them much more informative than mainstream forecasting methods like Monte Carlo simulations, used for instance by Facebook’s Prophet library.

2. Predict binary outcomes

A machine learning problem of great practical importance is to predict whether something will happen or not. This is a central problem in options trading, gambling, and finance (“will a recession happen?”).

Numerically, this problem translates to predicting 0 or 1 based on a set of input features.

Symbolic regression allows binary problems to be solved by using classification accuracy as the error metric for the search. In order to minimize the error, the optimization will converge without supervision towards formulas that only output 0 or 1, usually involving floor/ceil/round of some bounded function like tanh(x) or cos(x).

3. Predict continuous outcomes

A generalization of the problem of making a binary prediction is the problem of predicting a continuous quantity in the future.

For instance, in agriculture one could be interested in predicting the time for a crop to mature given parameters known at the time of sowing, such as soil composition, the month of the year, temperature, etc.

Usually, few data points will be available to train the model in this kind of scenario, but since symbolic models are simple, they are the least likely to overfit the data. The problem can be modeled by running the optimization with a standard error metric like root-mean-square error or mean error.

4. Solve classification problems

Classification problems, in general, can be solved by symbolic regression with a simple trick: representing different categorical variables as different integer numbers.

If your data points have 10 possible labels that should be predicted based on a set of input features, you can use symbolic regression to find formulas that output integers from 1 to 10 based on these features.

This may sound like asking too much — a formula capable of that is highly specific. But a good symbolic regression engine will be thorough in its search over the space of all mathematical formulas and will eventually find appropriate solutions.

5. Classify rare events

Another interesting case of classification problem is that of a highly imbalanced dataset, in which only a handful of rows contain the relevant label and the rest are negatives. This could be medical diagnostic images or fraudulent credit card transactions.

For this kind of problem, the usual classification accuracy search metric is not appropriate, since f(x1, x2, x3, …) = 0 will have a very high accuracy while being a useless function.

Special search metrics exist for this kind of problem, the most popular of which being the F1 score, which consists of the geometric mean between precision and recall. This search metric is available in TuringBot, allowing this kind of problem to be easily modeled.

6. Compress data

A mathematical formula is perhaps the shortest possible representation of a dataset. If the target variable features some kind of regularity, symbolic regression can turn gigabytes of data into something that can be equivalently expressed in one line.

Examples of target variables could be rgb colors of an image as a function of (x, y) pixels. We have tried finding a formula for the Mona Lisa, but unfortunately, nothing simple could be found in this case.

7. Interpolate data

Say you have a table of numbers and you want to compute the target variable for intermediate values not present in the table itself.

One way to go about this is to generate a spline interpolation from the table, which is a somewhat cumbersome and non-portable solution.

With symbolic regression, one can turn the entire table into a mathematical expression, and then proceed to do the interpolation without the need for specialized libraries or data structures, and also without the need to store the table itself anywhere.

8. Discover upper or lower bounds for a function

In problems of engineering and applied mathematics, one is often interested not in the particular value of a variable but in how fast this variable grows or how large it can be given an input. In this case, it is more informative to obtain an upper bound for the function than an approximation for the function itself.

With symbolic regression, this can be accomplished by discarding formulas that are not always larger or always smaller than the target variable. This kind of search is available out of the box in TuringBot with its “Bound search mode” option.

9. Discover the most predictive variables

When creating a machine learning model, it is extremely useful to know which input variables are the most relevant in predicting the target variable.

With black-box methods like neural networks, answering this kind of question is nontrivial because all variables are used at once indiscriminately.

But with symbolic regression the situation is different: since the formulas are kept as short as possible, variables that are not predictive end up not appearing, making it trivial to spot which variables are actually predictive and relevant.

10. Explore the limits of computability

Few people are aware of this, but the notion of computability has been first introduced by Alan Turing himself in his famous paper “On Computable Numbers, with an Application to the Entscheidungsproblem“.

Some things are easy to compute, for instance the function f(x) = x or common functions like sin(x) and exp(x) that can be converted into simple series expansions. But other things are much harder to compute, for instance, the N-th prime number.

With symbolic regression, one can try to derandomize tables of numbers and discover highly nonlinear patterns connecting variables. Since this is done in a very free way, even absurd solutions like tan(tan(tan(tan(x)))) end up being a possibility. This makes the method operate on the edge of computability.

Interested in symbolic regression? Download TuringBot and get started today.

Symbolic Regression in Python with TuringBot

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

For that, we are going to use the symbolic regression software TuringBot. This program runs on both Windows and Linux, and it comes with a handy Python library. You can download it for free from the official website.

Importing TuringBot

The first step in running our symbolic regression optimization in Python is importing TuringBot. For that, all you have to do is add its installation directory to your Python PATH and import it, as so:


import sys 
sys.path.insert(1, r'C:\Users\user\AppData\Local\Programs\TuringBot') 

import turingbot as tb 

import sys 
sys.path.insert(1, '/usr/share/turingbot') 

import turingbot as tb 

Running the optimization

The turingbot library implements a simulation object that can be used to start, stop and get the current status of a symbolic regression optimization.

This is how it works:


path = r'C:\Users\user\AppData\Local\Programs\TuringBot\TuringBot.exe' 
input_file = r'C:\Users\user\Desktop\input.txt' 
config_file = r'C:\Users\user\Desktop\settings.cfg' 

sim = tb.simulation() 
sim.start_process(path, input_file, threads=4, config=config_file) 

path = r'/usr/bin/turingbot' 
input_file = r'/home/user/input.txt' 
config_file = r'/home/user/settings.cfg' 

sim = tb.simulation() 
sim.start_process(path, input_file, threads=4, config=config_file) 

The start_process method starts the optimization in the background. It takes as input the paths to the TuringBot executable and to your input file. Optionally, you can also set the number of threads that the program should use and the path to the configuration file (more on that below).

After running the commands above, nothing will happen because the optimization will start in the background. To retrieve and print the current best formulas, you should use:

print(*sim.functions, sep='\n') 

To stop the optimization and kill the TuringBot process, you should use the terminate_process method:


Using a configuration file

We have seen above that the start_process method may take the path to a configuration file as an optional input parameter. This is what the file should look like:

4 # Search metric. 1: Mean relative error, 2: Classification accuracy, 3: Mean error, 4: RMS error, 5:, F1 score, 6: Correlation coefficient, 7: Hybrid (CC+RMS), 8: Maximum error, 9: Maximum relative error
-1 # Train/test split. -1: No cross validation. Valid options are: 50, 60, 70, 75, 80
1 # Test sample. 1: Chosen randomly, 2: The last points
0 # Integer constants only. 0: Disabled, 1: Enabled
0 # Bound search mode. 0: Deactivated, 1: Lower bound search, 2: Upper bound search
60 # Maximum formula complexity.
+ * / pow fmod sin cos tan asin acos atan exp log log2 sqrt sinh cosh tanh asinh acosh atanh abs floor ceil round tgamma lgamma erf # Allowed functions.

The comments after the # characters are for your convenience and are ignored. To change the search settings, all you have to do is change the numbers in each line. To change the base functions for the search, just add or delete their names from the last line.

Save the contents of the file above to a settings.cfg file and add the path of this file to the start_process method before calling it if you want to customize your search.

Full example

Here are the full source codes of the examples that we have provided above. Note that you have to replace user in the paths to your local username and that you have to create an input file (txt or csv format, one number per column) to use with the program.


import sys 
sys.path.insert(1, r'C:\Users\user\AppData\Local\Programs\TuringBot') 

import turingbot as tb 
import time

path = r'C:\Users\user\AppData\Local\Programs\TuringBot\TuringBot.exe' 
input_file = r'C:\Users\user\Desktop\input.txt' 
config_file = r'C:\Users\user\Desktop\settings.cfg' 

sim = tb.simulation() 
sim.start_process(path, input_file, threads=4, config=config_file) 


print(*sim.functions, sep='\n')



import sys 
sys.path.insert(1, '/usr/share/turingbot') 

import turingbot as tb 
import time 

path = r'/usr/bin/turingbot' 
input_file = r'/home/user/input.txt' 
config_file = r'/home/user/settings.cfg' 

sim = tb.simulation() 
sim.start_process(path, input_file, threads=4, config=config_file) 


print(*sim.functions, sep='\n') 


Eureqa vs TuringBot for symbolic regression

Introduced 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.

In 2017 this software was acquired by a global consulting company called DataRobot and left the market. The promise of revolutionizing physics was never quite fulfilled, but the project had a major impact in raising awareness about symbolic regression.

Here we want to compare Eureqa to a more recent symbolic regression software called TuringBot.

About TuringBot

Similarly to Eureqa, TuringBot is a symbolic regression software. It has a simple graphical interface that allows the user to load a dataset and then try to find formulas that predict a target column taking as input the remaining columns:

The TuringBot interface.

This software was introduced in 2020, and contrary to Eureqa it does not use a genetic algorithm to search for formulas, but instead a novel algorithm based on simulated annealing. While most references to symbolic regression in the literature involve genetic algorithms, our finding was that simulated annealing yields results much faster if implemented the right way.

Simulated annealing is inspired by a metallurgic process in which a metal is heated to a high temperature and then slowly cooled to attain better physical properties. The algorithm starts at first very “hot”, with worse solutions being accepted very often, and over time it cools down and becomes more strict about the solutions that it passes by. This allows the algorithm to overcome local maxima and discover the global maximum in a stochastic way.

Pareto optimization

Both TuringBot and Eureqa implement the idea searching for the best formulas of each possible size, and not just a single optimal formula. This is the essence of a Pareto optimization, and it results on a list of formulas of increasing complexity and accuracy to choose from.

A list of formulas of increasing complexity discovered by TuringBot.

A handy feature offered by TuringBot is to create a train/test split for the optimization and see in real-time the test error for the solutions discovered so far. This allows overfit solutions to be spotted very easily.


TuringBot is available for both Windows and Linux. It can be downloaded for free, but it also has a paid plan with more functionalities.

The software is already being used by many researchers and engineers around the world to study topics including turbine design, materials science and zoology, and also by business owners to come up with pricing models and other applications.

You might also like our article on Symbolic Regression featured on Towards Data Science: Symbolic Regression: The Forgotten Machine Learning Method.

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:

Clearly, we could hand draw a line that separates the two colors. But can this problem be solved in an automatic way?

Several machine learning methods could be used for this, including for instance a Support Vector Machine or AdaBoost. What all of these methods have in common is that they perform complex calculations under the hood and spill out some number, that is, they are black boxes. An interesting comparison of several of these methods can be found here.

A simpler and more elegant alternative is to try to find an explicit mathematical formula that separates the two categories. Not only would this be easier to compute, but it would also offer some insight into the data. This is where symbolic regression comes in.

Symbolic regression

The way to solve this problem with symbolic regression is to look for a formula that returns 0 for points of one category and 1 for points of another. That is, a formula for classification = f(x, y).

We can look for that formula by generating a CSV file with our points and loading it into TuringBot. Then we can run the optimization with classification accuracy as the search metric.

If we do that, the program ends up finding a simple formula with an accuracy of 100%:

classification = ceil(-1*tanh(round(x*y-cos((-2)*(y-x)))))

To visualize the decision boundary associated with this formula, we can generate some random points and keep track of the ones classified as orange. Then we can find the alpha shape that encompasses those points, which will be the decision boundary:

import alphashape
from descartes import PolygonPatch
import numpy as np
from math import *

def f(x, y):
    return ceil(-1*tanh(round(x*y-cos((-2)*(y-x)))))

pts = []
for i in range(10000):
    x = np.random.random()*2-1
    y = np.random.random()*2-1
    if f(x, y) == 1:
        pts.append([x, y])
pts = np.array(pts)

alpha_shape = alphashape.alphashape(pred, 2.)

fig, ax = plt.subplots()
ax.add_patch(PolygonPatch(alpha_shape, alpha=0.2, fc='#ddd', zorder=100))

And this is the result:

It is worth noting that even though this was a 2D problem, the same procedure could have been carried out for a classification problem in any number of dimensions.

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.

In this tutorial, we are going to show how an AI trading system can be created using a technique called symbolic regression. The idea will be to try to find a formula that classifies whether the price of a stock will rise or fall in the following day based on its price candles (open, high, low, close) in the last 14 days.

AI trading system concept

Our AI trading system will be a classification algorithm: it will take past data as input, and output 0 if the stock is likely to fall in the following day and 1 if it is likely to rise. The first step in generating this model is to prepare a training dataset in which each row contains all the relevant past data and also a 0 or 1 label based on what happened in the following day.

We can be very creative about what past data to use as input while generating the model. For instance, we could include technical indicators such as RSI and MACD, sentiment data, etc. But for the sake of this example, all we are going to use are the OHLC prices of the last 14 candles.

Our training dataset should then contain the following columns:


Here the index 1 denotes the last trading day, the index 2 the trading day prior to that, etc.

Generating the training dataset

To make things interesting, we are going to train our model on data for the S&P 500 index over the last year, as retrieved from Yahoo Finance. The raw dataset can be found here: S&P 500.csv.

To process this CSV file into the format that we need for the training, we have created the following Python script which uses the Pandas library:

import pandas as pd

df = pd.read_csv('S&P 500.csv')

training_data = []

for i,row in df.iterrows():
    if i < 13 or i+1 >= len(df):

    features = []
    for j in range(i, i-14, -1):
    if df.iloc[i+1]['Close'] > row['Close']:
columns = []
for i in range(1, 15):
    columns.append('open_%d' % i)
    columns.append('high_%d' % i)
    columns.append('low_%d' % i)
    columns.append('close_%d' % i)

training_data = pd.DataFrame(training_data, columns=columns)

training_data.to_csv('training.csv', index=False)

All this script does is iterate through the rows in the Yahoo Finance data and generate rows with the OHLC prices of the last 14 candles, and an additional ‘label’ column based on what happened in the following day. The result can be found here: training.csv.

Creating a model with symbolic regression

Now that we have the training dataset, we are going to try to find formulas that predict what will happen to the S&P 500 in the following day. For that, we are going to use the desktop symbolic regression software TuringBot. This is what the interface of the program looks like:

The interface of the TuringBot symbolic regression software.

The input file is selected from the menu on the upper left. We also select the following settings:

  • Search metric: classification accuracy.
  • Test/train split: 50/50. This will allow us to easily discard overfit models.
  • Test sample: the last points. The other option is “chosen randomly”, which would make it easier to overfit the data due to autocorrelation.

With these settings in place, we can start the search by clicking on the play button at the top of the interface. The best solutions found so far will be shown in real time, ordered by complexity, and their out-of-sample errors can be seen by toggling the “show cross validation” button on the upper right.

After letting the optimization run for a few minutes, these were the models that were encountered:

Symbolic models found for predicting S&P 500 returns.

The one with the best ouf-of-sample accuracy turned out to be the one with size 23. Its win rate in the test domain was 60.5%. This is the model:

label = 1-floor((open_5-high_4+open_12+tan(-0.541879*low_1-high_1))/high_13)

It can be seen that it depends on the low and high of the current day, and also on a few key parameters of previous days.


In this tutorial, we have generated an AI trading signal using symbolic regression. This model had good out-of-sample accuracy in predicting what the S&P 500 would do the next day, using for that nothing but the OHLC prices of the last 14 trading days. Even better models could probably be obtained if more interesting past data was used for the training, such as technical indicators (RSI, MACD, etc).

You can generate your own models by downloading TuringBot for free from the official website. We encourage you to experiment with different stocks and timeframes to see what you can find.

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.

In this tutorial, we are going to show how to find formulas using the desktop symbolic regression software TuringBot, which is very easy to use.

How symbolic regression works

Symbolic regression starts from a set of base functions to be used in the search, such as addition, multiplication, sin(x), exp(x), etc, and then tries to combine those functions in all possible ways with the goal of finding a model that will be as accurate as possible in predicting a target variable. Some examples of base functions used by TuringBot are the following:

Some base functions that TuringBot uses for symbolic regression.

As important as the accuracy of a formula is its simplicity. A huge formula can predict with perfect accuracy the data points, but if the number of free parameters in the model is the same as the number of points then this model is not really informative. For this reason, a symbolic regression optimization will discard a larger formula if it finds a smaller one that performs just as well.

Finding a formula with TuringBot

Finding equations from data points with TuringBot is a simple process. The first step is selecting the input file with the data through the interface. This input file should be in TXT or CSV format. After it has been loaded, the target variable can be selected (by default it will be the last column in the file), and the search can be started. This is what the interface looks like:

The interface of the TuringBot symbolic regression software.

Several options are available on the menus on the left, such as setting a test/train split to be able to detect overfit solutions, selecting which base functions should be used, and selecting the search metric, which by default is root-mean-square error, but that can also be set to classification accuracy, mean relative error and others. For this example, we are going to keep it simple and just use the defaults.

The optimization is started by clicking on the play button at the top of the interface. The best formulas found so far will be shown in the solutions box, ordered by complexity:

The formulas found by TuringBot for an example dataset.

The software allows the solutions to be exported to common programming languages from the menu, and also to simply be exported as text. Here are the formulas in the example above exported in text format:

Complexity   Error      Function
1            1.91399    -0.0967549
3            1.46283    0.384409*x
4            1.362      atan(x)
5            1.18186    0.546317*x-1.00748
6            1.11019    asinh(x)-0.881587
9            1.0365     ceil(asinh(x))-1.4131
13           0.985787   round(tan(floor(0.277692*x)))
15           0.319857   cos(x)*(1.96036-x)*tan(x)
19           0.311375   cos(x)*(1.98862-1.02261*x)*tan(1.00118*x)


In this tutorial, we have seen how symbolic regression can be used to find formulas from values. Symbolic regression is very different from regular curve-fitting methods, since no assumption is made about what the shape of the formulas should be. This allows patterns to be found in datasets with an arbitrary number of dimensions, making symbolic regression a general purpose machine learning technique.

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.

What is a black box method?

A method is said to be a black box when it performs complicated computations under the hood that cannot be clearly explained and understood. Data is fed into the model, internal transformations are performed on this data and an output is given, but these transformations are such that basic questions cannot be answered in a straightforward way:

  • Which of the input variables contributed the most to generating the output?
  • Exactly what features did the model derive from the input data?
  • How does the output change as a function of one of the variables?

Not only are black box models hard to understand, they are also hard to move around: since complicated data structures are necessary for the relevant computations, they cannot be readily translated to different programming languages.

Can there be machine learning without black boxes?

The answer to that question is yes. In the simplest case, a machine learning model can be a linear regression and consist of a line defined by an explicit algebraic equation. This is not a black box method, since it is clear how the variables are being used to compute an output.

But linear models are quite limited and cannot perform the same kinds of tasks that neural networks do, for example. So a more interesting question is: is there a machine learning method capable of finding nonlinear patterns in an explicit and understandable way?

It turns out that such method exists, and is called symbolic regression.

Symbolic regression as an alternative

The idea of symbolic regression is to find explicit mathematical formulas that connect input variables to an output, while trying to keep those formulas as simple as possible. The resulting models end up being explicit equations that can be written on a sheet of paper, making it apparent how the input variables are being used despite the presence of nonlinear computations.

To give a clearer picture, consider some models found by TuringBot, a symbolic regression software for PC:

Symbolic models found by the TuringBot symbolic regression software.

In the “Solutions” box above, a typical result of a symbolic regression optimization can be seen. A set of formulas of increasing complexity was found, with more complex formulas only being shown if they perform better than all simpler alternatives. A nonlinearity in the input dataset was successfully recovered through the use of nonlinear base functions like cos(x), atan(x) and multiplication.

Symbolic regression is a very general technique: although the most obvious use case is to solve regression problems, it can also be used to solve classification problems by representing categorical variables as different integer numbers, and running the optimization with classification accuracy as the search metric instead of RMS error. Both of these options are available in TuringBot.


In this article, we have seen that despite most machine learning methods indeed being black boxes, not all of them are. A simple counterexample are linear models, which are explicit and hence not black boxes. More interestingly, we have seen how symbolic regression is capable of solving machine learning tasks where nonlinear patterns are present, generating models that are mathematical equations that can be analyzed and interpreted.

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.

What regression is

The goal of a regression model is to be able to predict a target variable taking as input one or more input variables. The simplest case is that of a linear relationship between the variables, in which case basic methods such as least squares regression can be used.

In real-world datasets, the relationship between the variables is often highly non-linear. This motivates the use of more sophisticated machine learning techniques to solve the regression problems, including for instance neural networks and random forests.

A regression problem example is to predict the value of a house from its characteristics (location, number of bedrooms, total area, etc), using for that information from other houses which are not identical to it but for which the prices are known.

Regression model example

To give a concrete example, let’s consider the following public dataset of house prices: x26.txt. This file contains a long and uncommented header; a stripped-down version that is compatible with TuringBot can be found here: house_prices.txt. The columns that are present are the following

Local selling prices, in hundreds of dollars;
Number of bathrooms;
Area of the site in thousands of square feet;
Size of the living space in thousands of square feet;
Number of garages;
Number of rooms;
Number of bedrooms;
Age in years;
Construction type (1=brick, 2=brick/wood, 3=aluminum/wood, 4=wood);
Number of fire places;
Selling price.

The goal is to predict the last column, the selling price, as a function of all the other variables. In order to do that, we are going to use a technique called symbolic regression, which attempts to find explicit mathematical formulas that connect the input variables to the target variable.

We will use the desktop software TuringBot, which can be downloaded for free, to find that regression model. The usage is quite straightforward: you load the input file through the interface, select which variable is the target and which variables should be used as input, and then start the search. This is what its interface looks like with the data loaded in:

The TuringBot interface.

We have also enabled the cross validation feature with a 50/50 test/train split (see the “Search options” menu in the image above). This will allow us to easily discard overfit formulas.

After running the optimization for a few minutes, the formulas found by the program and their corresponding out-of-sample errors were the following:

The regression models found for the house prices.

The highlighted one turned out to be the best — more complex solutions did not offer increased out-of-sample accuracy. Its mean relative error in the test dataset was of roughly 8%. Here is that formula:

price = fire_place+15.5668+(1.66153+bathrooms)*local_pric

The variables that are present in it are only three: the number of bathrooms, the number of fire places and the local price. It is a completely non-trivial fact that the house price should only depend on these three parameters, but the symbolic regression optimization made this fact evident.


In this tutorial, we have seen an example of generating a regression model. The technique that we used was symbolic regression, implemented in the desktop software TuringBot. The model that was found had a good out-of-sample accuracy in predicting the prices of houses based on their characteristics, and it allowed us to clearly see the most relevant variables in estimating that price.

Neural networks: what are the alternatives?

TuringBot is a great alternative to neural networks. Check it out!

In this article, we will see some alternatives to neural networks that can be used to solve the same types of machine learning tasks that they do.

What neural networks are

Neural networks are by far the most popular machine learning method. They are capable of automatically learning hidden features from input data prior to computing an output value, and established algorithms exist for finding the optimal internal parameters (weights and biases) based on a training dataset.

The basic architecture is the following. The building blocks are perceptrons, which take values as input, calculate a weighted sum of those values, and apply a non-linear activation function to the result. The output is then either fed into perceptrons of the next layer, or it is sent to the output if that was the last layer.

The basic architecture of a neural network. Blue circles are perceptrons.

This architecture is directly inspired by the workings of the human brain. Combined with a neural network’s ability to learn from data, a strong association between this machine learning method and the notion of artificial intelligence can be drawn.

Alternatives to neural networks

Despite being so popular, neural networks are not the only machine learning method available. Several alternatives exist, and in many contexts, these alternatives may provide better performances.

Some noteworthy alternatives are the following:

  • Random forests, which consist of an ensemble of decision trees, each trained with a random subset of the training dataset. This method corrects a decision tree’s tendency to overfit the input data.
  • Support vector machines, which attempt to map the input data into a space where it is linearly separable into different categories.
  • k-nearest neighbors algorithm (KNN), which looks for the values in the training dataset that are closest to a new input, and combines the target variables associated with those nearest neighbors into a new prediction.
  • Symbolic regression, a technique that tries to find explicit mathematical formulas that connect the input variables to the target variable.

A noteworthy alternative

Among the alternatives above, all but symbolic regression involve implicit computations under the hood that cannot be easily interpreted. With symbolic regression, the model is an explicit mathematical formula that can be written on a sheet of paper, making this technique an alternative to neural networks of particular interest.

Here is how it works: given a set of base functions, for instance, sin(x), exp(x), addition, multiplication, etc, a training algorithm tries to find the combinations of those functions that best predict the output variable taking as input the input variables. It is important that the formulas encountered are the simplest ones possible, so the algorithm will automatically discard a formula if it finds a simpler one that performs just as well.

Here is an example of output for a symbolic regression optimization, in which a set of formulas of increasing complexity were found that describe the input dataset. The symbolic regression package used is called TuringBot, a desktop application that can be downloaded for free.

Formulas found with a symbolic regression optimization.

This method very much resembles a scientist looking for mathematical laws that explain data, as Kepler did with data on the positions of planets in the sky to find his laws of planetary motion.


In this article, we have seen some alternatives to neural networks based on completely different ideas, including for instance symbolic regression which generates models that are explicit and more explainable than a neural network. Exploring different models is very valuable, because they may perform differently in different particular contexts.

TuringBot is a great alternative to neural networks. Check it out!

A free AI software for PC

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.

The software

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 interface of TuringBot.

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.

Symbolic regression

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.

Getting TuringBot

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.