Question about predictor output: Score and PredictedLabel columns #376

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Question about predictor output: Score and PredictedLabel columns#376
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documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project

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@pkulikov
pkulikov
opened on Jun 19, 2018

Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:

How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).

As for the trainer inputs, rules are more or less clear:

  • Use the Label column for labels (or specify another column name through the LabelColumn property)
  • Use the Features column for features (or specify another column name through the FeatureColumn property)

Can the setup of the predictor output be done in similar way:

  • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
  • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)

By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.

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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" + '

    Skip to content
    Question about predictor output: Score and PredictedLabel columns #376
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    Question about predictor output: Score and PredictedLabel columns#376
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    JRAlexander
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    documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
    Description
    @pkulikov
    pkulikov
    opened on Jun 19, 2018
    Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
    How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
    As for the trainer inputs, rules are more or less clear:
    • Use the Label column for labels (or specify another column name through the LabelColumn property)
    • Use the Features column for features (or specify another column name through the FeatureColumn property)
    Can the setup of the predictor output be done in similar way:
    • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
    • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
    By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
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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");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

      Skip to content
      Question about predictor output: Score and PredictedLabel columns #376
      Closed
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      Question about predictor output: Score and PredictedLabel columns#376
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      JRAlexander
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      documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
      Description
      @pkulikov
      pkulikov
      opened on Jun 19, 2018
      Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
      How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
      As for the trainer inputs, rules are more or less clear:
      • Use the Label column for labels (or specify another column name through the LabelColumn property)
      • Use the Features column for features (or specify another column name through the FeatureColumn property)
      Can the setup of the predictor output be done in similar way:
      • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
      • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
      By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
      Metadata
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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('^' + ".*" + '

        Skip to content
        Question about predictor output: Score and PredictedLabel columns #376
        Closed
        Closed
        Question about predictor output: Score and PredictedLabel columns#376
        Assignees
        JRAlexander
        Labels
        documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
        Description
        @pkulikov
        pkulikov
        opened on Jun 19, 2018
        Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
        How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
        As for the trainer inputs, rules are more or less clear:
        • Use the Label column for labels (or specify another column name through the LabelColumn property)
        • Use the Features column for features (or specify another column name through the FeatureColumn property)
        Can the setup of the predictor output be done in similar way:
        • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
        • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
        By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
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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" + '

          Skip to content
          Question about predictor output: Score and PredictedLabel columns #376
          Closed
          Closed
          Question about predictor output: Score and PredictedLabel columns#376
          Assignees
          JRAlexander
          Labels
          documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
          Description
          @pkulikov
          pkulikov
          opened on Jun 19, 2018
          Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
          How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
          As for the trainer inputs, rules are more or less clear:
          • Use the Label column for labels (or specify another column name through the LabelColumn property)
          • Use the Features column for features (or specify another column name through the FeatureColumn property)
          Can the setup of the predictor output be done in similar way:
          • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
          • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
          By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
          Metadata
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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('^' + ".*" + '

            Skip to content
            Question about predictor output: Score and PredictedLabel columns #376
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            Question about predictor output: Score and PredictedLabel columns#376
            Assignees
            JRAlexander
            Labels
            documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
            Description
            @pkulikov
            pkulikov
            opened on Jun 19, 2018
            Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
            How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
            As for the trainer inputs, rules are more or less clear:
            • Use the Label column for labels (or specify another column name through the LabelColumn property)
            • Use the Features column for features (or specify another column name through the FeatureColumn property)
            Can the setup of the predictor output be done in similar way:
            • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
            • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
            By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
            Metadata
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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('^' + ".*" + '

              Skip to content
              Question about predictor output: Score and PredictedLabel columns #376
              Closed
              Closed
              Question about predictor output: Score and PredictedLabel columns#376
              Assignees
              JRAlexander
              Labels
              documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
              Description
              @pkulikov
              pkulikov
              opened on Jun 19, 2018
              Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
              How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
              As for the trainer inputs, rules are more or less clear:
              • Use the Label column for labels (or specify another column name through the LabelColumn property)
              • Use the Features column for features (or specify another column name through the FeatureColumn property)
              Can the setup of the predictor output be done in similar way:
              • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
              • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
              By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
              Metadata
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              Assignees
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              Type
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, '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); }
})();
})();

                Skip to content
                Question about predictor output: Score and PredictedLabel columns #376
                Closed
                Closed
                Question about predictor output: Score and PredictedLabel columns#376
                Assignees
                JRAlexander
                Labels
                documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
                Description
                @pkulikov
                pkulikov
                opened on Jun 19, 2018
                Current two tutorials in the docs use different columns to get a predicted value out of the pipeline into an instance of the user-defined prediction type:
                How does one know which column to use to populate instances of the prediction type? Especially given that, in case of the (binary) classification solution, the Score column is also available (I guess, then it contains the probabilities of being in a certain class).
                As for the trainer inputs, rules are more or less clear:
                • Use the Label column for labels (or specify another column name through the LabelColumn property)
                • Use the Features column for features (or specify another column name through the FeatureColumn property)
                Can the setup of the predictor output be done in similar way:
                • Use the column with the same name across all the predictors for the predictor output. I guess that might require to extend regression IDataView with the PredictedLabel column that would be a copy of the Score column.
                • Be able to setup the name of the output column. (That seems the PredictedLabelColumnOriginalValueConverter can be used for that; or I'm wrong and that class is intended for use in tandem with the Dictionarizer?)
                By the way, the mere explanation of the Score and PredictedLabel columns here would be appreciated as well. Then, at least, I'll update the docs to make story clearer.
                Metadata
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                documentationRelated to documentation of ML.NETquestionFurther information is requestedup-for-grabsA good issue to fix if you are trying to contribute to the project
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