Skip to content
BranchesTags

Latest commit

History

20 Commits

Folders and files

NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py

Repository files navigation

StructureBoost

StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.

Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"

Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)

StructureBoost can help. Read the documentation and references below. Or dive into some examples

Video Lectures

There are some explanatory videos on the Numeristical Youtube Channel

Documentation

Read the Docs

References:

Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf

Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446

About

Gradient boosting using categorical structure

Resources

Readme
MIT license
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - nesuter/structureboost: Gradient boosting using categorical structure · GitHub
Skip to content
BranchesTags
Latest commit
History
20 Commits
Folders and files
NameName
Last commit message
Last commit date
docs
docs
examples
examples
structureboost
structureboost
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
README.rst
README.rst
setup.py
setup.py
Repository files navigation
StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the structure of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some examples
Video Lectures
There are some explanatory videos on the Numeristical Youtube Channel
Documentation
Read the Docs
References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." https://arxiv.org/abs/2007.04446
About
Gradient boosting using categorical structure
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages