chainladder.BornhuetterFerguson

chainladder.BornhuetterFerguson#

class chainladder.BornhuetterFerguson(apriori=1.0, apriori_sigma=0.0, random_state=None)#

The deterministic Bornhuetter Ferguson IBNR model

Parameters:

apriori: float, optional (default=1.0): Multiplier for the sample_weight used in the Bornhuetter Ferguson method. If sample_weight is already an apriori measure of ultimate, then use 1.0
apriori_sigma: float, optional (default=0.0): Standard deviation of the apriori. When used in conjunction with the bootstrap model, the model samples aprioris from a lognormal distribution using this argument as a standard deviation.
random_state: int, RandomState instance or None, optional (default=None): Seed for sampling from the apriori distribution. This is ignored when using as a deterministic method. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

Methods

`fit`(X[, y, sample_weight])	Applies the Bornhuetter-Ferguson technique to triangle X
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`intersection`(a, b)	Given two Triangles with mismatched indices, this method aligns their indices
`predict`(X[, sample_weight])	Predicts the Bornhuetter-Ferguson ultimate on a new triangle X
`set_backend`(backend[, inplace, deep])	Converts triangle array_backend.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, sample_weight])	Request metadata passed to the `predict` method.
`to_json`()	Serializes triangle object to json format
`to_pickle`(path[, protocol])	Serializes triangle object to pickle.