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Extremely Random Trees

What

Extremely Random Trees is a machine-learning algorithm described in:

"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,
by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005

This is my own implementation of that algorithm as a friendly terminal program. It can be compiled and executed on any modern Unix/Linux/OSX computer.

Building

I've provided a Makefile which should do the job. After you've downloaded this repository, get into its directory in a terminal and do:

make

Usage

  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.

Example: Growing a Model

./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.

Example: Applying a Model

./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.

Advanced Options

etgrow uses reasonable default values for the algorithm hyperparameters, but it is also capable of finding the best values for all of the hyperparameters if you have a few minutes to let it think.
To see a complete list of program options, just run etgrow with no command-line parameters.

About

My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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" + '

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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Extremely Random Trees
What
Extremely Random Trees is a machine-learning algorithm described in:
"Extremely randomized trees", DOI 10.1007/s10994-006-6226-1,

by Pierre Geurts, Damien Ernst, Louis Wehenkel, 2005
This is my own implementation of that algorithm as a friendly terminal program.
It can be compiled and executed on any modern Unix/Linux/OSX computer.
Building
I've provided a Makefile which should do the job.
After you've downloaded this repository, get into its directory in a terminal and do:
make

Usage
  1. You provide training data.
  • Training data is a list of sets of values.
  • One set of values is one or more independent variables, and exactly one dependent variable.
  1. You format the data into an ASCII (text) file following the comma-separated-value format (CSV).
  • The first line of the file is an ordered list of variable names.
  • Each following line represents one example, which is a complete set of values in the same order as the variable names.
  1. You run etgrow using that file as a training file, and specifying where you'd like the output model to go.
  2. If you have more data for which the dependent variable is unknown, then you run etpredict using the model from etgrow to make predictions about the values of the dependent variable.
Example: Growing a Model
./etgrow -t data/spambase-train.csv -m spambase-model

spambase-train.csv is an example input training file, provided in this repository.
spambase-model is the output model file that etgrow will create, and can be named whatever you want.
Example: Applying a Model
./etpredict -m spambase.model -t data/spambase-test.csv -p spambase.predictions

spambase.model is a model file that you created with etgrow at some point.
spambase-test.csv is an example input testing file, provided in this repository.
spambase.predictions is the output predictions file that etpredict will create, and can be named whatever you want. It contains an ordered list of dependent variable values.
Advanced Options
etgrow uses reasonable default values for the algorithm hyperparameters,
but it is also capable of finding the best values for all of the hyperparameters
if you have a few minutes to let it think.

To see a complete list of program options, just run etgrow with no command-line parameters.
About
My implementation of the Extremely Random Trees machine-learning algorithm (Pierre Geurts, et al, 2005)
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