fit_sklearn()

Fit a sklearn model with output that is compatible with pymarginaleffects.

This function streamlines the process of fitting sklearn models by:

1. Parsing the formula
2. Handling missing values
3. Creating model matrices
4. Fitting the model with specified options

Parameters

formula: (str) Model formula

• Example: “outcome ~ distance + incentive”

data: (pandas.DataFrame) Dataframe with the response variable and predictors.

engine: (callable) sklearn model class (e.g., LinearRegression, LogisticRegression)

kwargs_engine: (dict, default={}) Additional arguments passed to the model initialization.

• Example: {'weights': weights_array}

kwargs_fit : (dict, default={}) Additional arguments passed to the model’s fit method.

Returns

(ModelSklearn) A fitted model wrapped in the ModelSklearn class for compatibility with marginaleffects.

Examples

from sklearn.linear_model import LinearRegression
from marginaleffects import *

data = get_dataset()

model = fit_sklearn(
    formula="outcome ~ distance + incentive",
    data=data,
    engine=LinearRegression,
)

predictions(model)

Notes

The fitted model includes additional attributes:

• data: The processed data after listwise deletion
• formula: The original formula string
• formula_engine: Set to “sklearn”
• model: The fitted sklearn model object