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📊 Precision-Recall Explorer for Business

Shiny AppBlog Post

Unveiling the Dynamics of Precision and Recall

Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.

Key Features

  1. Confusion Matrix Playground:

    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.

  2. Performance Breakdown:

    • Get a closer look at your model's performance with key metrics:
      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.

  3. Metrics in Plain Language:

    • Understand the metrics in business terms:
      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.

Your Business, Your Story

  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.

How to Use

  1. Clone or Download:

    • Clone the repository or download the ZIP file to get started.

  2. Run Locally:

    • Open the Shiny app in RStudio or your preferred environment.

  3. Interact and Learn:

    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.

Why Metrics Explorer?

Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:

  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.

Contribute

  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.

License

This project is licensed GNU GENERAL PUBLIC LICENSE.

About

Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.

tiger-tang.shinyapps.io/MetricsExplorer/

Topics

classificationmachine-learningprecision-recallrshiny

Resources

Readme
GPL-3.0 license
Activity

Stars

0 stars

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 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n 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;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, '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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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, '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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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, '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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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
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1 watching
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0 forks
Report repository
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, '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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README.MD
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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
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📊 Precision-Recall Explorer for Business
Shiny AppBlog Post
Unveiling the Dynamics of Precision and Recall
Welcome to the Metrics Explorer – an interactive Shiny app designed to demystify the world of precision and recall for business stakeholders. Explore it here.
Key Features
  1. Confusion Matrix Playground:
    • Visualize the interplay between precision and recall on a dynamic confusion matrix.
    • Explore the impact of tweaking these metrics on True Positives, True Negatives, False Positives, and False Negatives.
  2. Performance Breakdown:
    • Get a closer look at your model's performance with key metrics:

      • Successful Predictions (True Positives) – the wins.
      • Accurate Non-events (True Negatives) – the silent guardians.
      • False Alarms (False Positives) – the surprises.
      • Missed Opportunities (False Negatives) – the near-misses.
  3. Metrics in Plain Language:
    • Understand the metrics in business terms:

      • True Positives (TP): Successful Predictions.
      • True Negatives (TN): Accurate Non-events.
      • False Positives (FP): False Alarms.
      • False Negatives (FN): Missed Opportunities.
Your Business, Your Story
  • Tailored Scenarios: Customize discussions to fit your business context.
  • Use Case Integration: Whether it's loan default prediction or customer churn, see how precision and recall play out in your unique scenario.
How to Use
  1. Clone or Download:
    • Clone the repository or download the ZIP file to get started.
  2. Run Locally:
    • Open the Shiny app in RStudio or your preferred environment.
  3. Interact and Learn:
    • Adjust the sliders for total sample, proportion of actual positives, precision, and recall.
    • Witness how changes impact the confusion matrix and key performance metrics.
Why Metrics Explorer?
Data Science often hits a "communication wall" when explaining model thresholds. This app breaks that wall by:
  • Translating Jargon: Replaces TP/FP/TN/FN with business-friendly terms like "Wins" and "Surprises."
  • Interactive Simulation: Let stakeholders "feel" the trade-off by moving sliders for precision and recall.
  • Contextual Discussion: Ideal for "What-If" sessions regarding loan defaults, medical screening, or customer churn.
Contribute
  • Issues and Contributions: If you find a bug or have an idea for improvement, open an issue or submit a pull request.
License
This project is licensed GNU GENERAL PUBLIC LICENSE.
About
Interactive Shiny app for explaining precision–recall tradeoffs to business stakeholders.
tiger-tang.shinyapps.io/MetricsExplorer/
Topics
classificationmachine-learningprecision-recallrshiny
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages