https://github.com/casact/tryangle

Tryangle is an automatic chainladder reserving framework. It provides scoring and optimisation methods based on machine learning techniques to automatically select optimal parameters to minimise reserve prediction error.
https://github.com/casact/tryangle

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Tryangle is an automatic chainladder reserving framework. It provides scoring and optimisation methods based on machine learning techniques to automatically select optimal parameters to minimise reserve prediction error.

• Host: GitHub
• URL: https://github.com/casact/tryangle
• Owner: casact
• License: mpl-2.0
• Created: 2021-05-05T10:39:45.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-06-11T18:04:18.000Z (over 1 year ago)
• Last Synced: 2024-05-17T02:58:59.610Z (4 months ago)
• Language: Python
• Homepage:
• Size: 89.8 KB
• Stars: 17
• Watchers: 4
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• jimsghstars - casact/tryangle - Tryangle is an automatic chainladder reserving framework. It provides scoring and optimisation methods based on machine learning techniques to automatically select optimal parameters to minimise reser (Python)

README

        
# Tryangle: Machine Learning Techniques for Chainladder Methods

*Tryangle* is an automatic chainladder reserving framework. It provides

scoring and optimisation methods based on machine learning techniques to

automatically select optimal parameters to minimise reserve prediction

error. Tryangle is built on top of the

[chainladder](https://chainladder-python.readthedocs.io/en/latest/index.html)

reserving package.

## Key Features

Tryangle is flexible and modular in how it can be applied:

-   Optimising loss development factors

    -   Use [sklearn's](https://scikit-learn.org/) GridSearchCV or RandomizedSearchCV to find the optimal

        method to calculate loss development factors

-   Choosing between multiple reserving methods

    -   Not sure if you should go with a basic chainladder,

        Bornhuetter-Ferguson, or Cape-Cod method? Let Tryangle decide.

-   Finding the optimal blend of reserving methods

    -   Or why not combine all three, and let Tryangle find the optimal

        blend.

-   Using advanced optimisation methods

    -   Not satisfied with an exhaustive grid search? Tryangle can be

        used with any optimisation framework, but we recommend

        [Optuna](https://optuna.org/)

## Basic Example

``` python

from sklearn.model_selection import GridSearchCV

from sklearn.pipeline import Pipeline

from tryangle import Development, CapeCod

from tryangle.metrics import neg_cdr_scorer

from tryangle.model_selection import TriangleSplit

from tryangle.utils.datasets import load_sample

X = load_sample("swiss")

tscv = TriangleSplit(n_splits=5)

param_grid = {

    "dev__n_periods": range(15, 20),

    "dev__drop_high": [True, False],

    "dev__drop_low": [True, False],

    "cc__decay": [0.25, 0.5, 0.75, 0.95],

}

pipe = Pipeline([("dev", Development()), ("cc", CapeCod())])

model = GridSearchCV(

    pipe, param_grid=param_grid, scoring=neg_cdr_scorer, cv=tscv, verbose=1, n_jobs=-1

)

model.fit(X, X)

```

## Installation

Tryangle is available at [the Python Package

Index](https://pypi.org/project/tryangle/).

``` console

pip install tryangle

```

Tryangle supports Python 3.9.

## Reference

Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A

Machine Learning Approach

([SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3697256)).