Sklearn-compatible API

If you want to use Genetic Engine in your Machine Learning pipelines, you can use it, through these examples:

Classification

from geml.classifiers import GeneticProgrammingClassifier

model = GeneticProgrammingClassifier()
model.fit(X, y)

Regression

from geml.regressors import GeneticProgrammingRegressor

model = GeneticProgrammingRegressor()
model.fit(X, y)

For complete examples, check the sklearn-type-examples.py in the examples folder.

Feature probability weighting

All sklearn-compatible estimators accept an optional parameter weight_features_by_correlation (default: False). When enabled, terminals corresponding to input features are sampled with probabilities proportional to their absolute Pearson correlation with the output, biasing the grammar towards more predictive variables.