# 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)
```

```{eval-rst}
.. autoapiclass:: geml.classifiers.GeneticProgrammingClassifier
```

## Regression

```
from geml.regressors import GeneticProgrammingRegressor

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

```{eval-rst}
.. autoapiclass:: geml.regressors.GeneticProgrammingRegressor
```


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.