Eureqa automatically splits your data into groups: training and validation data sets. The training data is used to optimize models, whereas validation data is used to test how well models generalize to new data. Eureqa also uses the validation data to filter out the best models to display in the Eureqa user interface. This post describes how to use and control these data sets in Eureqa.
Several features in Eureqa assume that your data is one continuous series of points by default, such as the smoothing features and numerical derivative operators. This post shows how to tell Eureqa that there are breaks in the data.
Eureqa's Search Relation setting provides quite a bit of flexibility to search for different types of models. This post describes some advanced techniques of using the Search Relation setting to specify custom error metrics for the search to optimize; or more specifically, arbitrary custom loss functions for the fitness calculation.
A time delay retrieves the value of a variable or expression at a fixed offset in the past, according to the time ordering or index of each data point in the data set. This post describes the time-delay building-blocks available in Eureqa and different modeling techniques with delayed values.
Eureqa can automatically estimate numerical derivatives in order to model the rates of change of variables in your data. Often derivatives are more natural and simpler for modeling certain types of phenomena, particularly in physics. This post discusses the basics of entering derivatives into the Eureqa search relationship.
While normalizing your data variables (rescaling the numeric values) is completely optional, it can greatly improve the performance of Eureqa, and numerical stability of solutions. This post discusses when and how to normalize variables in your data.
Binary classification attempts to predict a variable that has only two possible outcomes - for example, true or false, or buy or don't buy. This post describes how Eureqa can be used to model a boolean decision or classification value.
Often you might want to specify that the output of a model should fall within a certain range rather than an exact numerical value. This post shows one way to do this with Eureqa. The goal it so find the simplest equations who's outputs always lie between some min/max value for each data point.