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2024-11-18 15:30:19

You will notice that expressions are printed using the column names of our table. If, instead of a table-like object, a simple array is passed (e.g., X=randn(100, 2)), x1, ..., xn will be used for variable names.

This will make predictions using the expression selected by model.selection_method, which by default is a mix of accuracy and complexity.

For fitting multiple outputs, one can use MultitargetSRRegressor (and pass an array of indices to idx in predict for selecting specific equations). For a full list of options available to each regressor, see the API page.

The heart of SymbolicRegression.jl is the equation_search function. This takes a 2D array and attempts to model a 1D array using analytic functional forms. Note: unlike the MLJ interface, this assumes column-major input of shape [features, rows].

Expressions are represented under-the-hood as the Node type which is developed in the DynamicExpressions.jl package. The Expression type wraps this and includes metadata about operators and variable names.