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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 reserving package.

Key Features

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

  • Optimising loss development factors
    • Use sklearn'sGridSearchCV 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

Basic Example

fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.

pip install tryangle

Tryangle supports Python 3.9.

Reference

Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A Machine Learning Approach (SSRN).

About

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.

Resources

Readme
MPL-2.0 license
Activity
Custom properties

Stars

28 stars

Watchers

4 watching

Forks

13 forks
Report repository

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Used by

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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
28 stars
Watchers
4 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
28 stars
Watchers
4 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
28 stars
Watchers
4 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
28 stars
Watchers
4 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
28 stars
Watchers
4 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
Resources
Readme
MPL-2.0 license
Activity
Custom properties
Stars
28 stars
Watchers
4 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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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
reserving package.
Key Features
Tryangle is flexible and modular in how it can be applied:
  • Optimising loss development factors

    • Use sklearn'sGridSearchCV 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
Basic Example
fromsklearn.model_selectionimportGridSearchCVfromsklearn.pipelineimportPipelinefromtryangleimportDevelopment, CapeCodfromtryangle.metricsimportneg_cdr_scorerfromtryangle.model_selectionimportTriangleSplitfromtryangle.utils.datasetsimportload_sampleX=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.
pip install tryangle
Tryangle supports Python 3.9.
Reference
Caesar Balona, Ronald Richman. 2021. The Actuary and IBNR Techniques: A
Machine Learning Approach
(SSRN).
About
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. 
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