WeightedProportion

class causalpy.skl_models.WeightedProportion

Model which minimises sum squared error subject to:

• All weights are bound between 0-1

• Weights sum to 1.

Inspiration taken from this blog post https://towardsdatascience.com/understanding-synthetic-control-methods-dd9a291885a1

Methods

WeightedProportion.__init__(*args, **kwargs)
WeightedProportion.fit(X, y)	Fit model on data X with predictor y
WeightedProportion.get_metadata_routing()	Get metadata routing of this object.
WeightedProportion.get_params([deep])	Get parameters for this estimator.
WeightedProportion.loss(W, X, y)	Compute root mean squared loss with data X, weights W, and predictor y
WeightedProportion.predict(X)	Predict results for data X
WeightedProportion.score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
WeightedProportion.set_params(**params)	Set the parameters of this estimator.
WeightedProportion.set_score_request(*[, ...])	Request metadata passed to the score method.

__init__(*args, **kwargs)

__new__(*args, **kwargs)