FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498

Closed
Closed
FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498

Description

@JoshuaSloan
JoshuaSloan
opened on Sep 9, 2025

System Information (please complete the following information):

  • OS & Version: Windows 11
  • ML.NET Version: ML.NET v4.0.2
  • .NET Version: .NET 9.0

Describe the bug
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.

To Reproduce
Steps to reproduce the behavior:

  1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
  2. Set all non-FastForest trainers to false (such that only FastForest remains)
  3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
  4. Compare the best model to the reported parameters

Expected behavior
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.

Screenshots, Code, Sample Projects
Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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
      FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
      Closed
      Closed
      FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
      Description
      @JoshuaSloan
      JoshuaSloan
      opened on Sep 9, 2025
      System Information (please complete the following information):
      • OS & Version: Windows 11
      • ML.NET Version: ML.NET v4.0.2
      • .NET Version: .NET 9.0
      Describe the bug
      
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
      To Reproduce
      
Steps to reproduce the behavior:
      1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
      2. Set all non-FastForest trainers to false (such that only FastForest remains)
      3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
      4. Compare the best model to the reported parameters
      Expected behavior
      
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
      Screenshots, Code, Sample Projects
      Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
      
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
      Image
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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('^' + ".*" + '

          Skip to content
          FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
          Closed
          Closed
          FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
          Description
          @JoshuaSloan
          JoshuaSloan
          opened on Sep 9, 2025
          System Information (please complete the following information):
          • OS & Version: Windows 11
          • ML.NET Version: ML.NET v4.0.2
          • .NET Version: .NET 9.0
          Describe the bug
          
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
          To Reproduce
          
Steps to reproduce the behavior:
          1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
          2. Set all non-FastForest trainers to false (such that only FastForest remains)
          3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
          4. Compare the best model to the reported parameters
          Expected behavior
          
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
          Screenshots, Code, Sample Projects
          Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
          
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
          Image
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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
              FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
              Closed
              Closed
              FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
              Description
              @JoshuaSloan
              JoshuaSloan
              opened on Sep 9, 2025
              System Information (please complete the following information):
              • OS & Version: Windows 11
              • ML.NET Version: ML.NET v4.0.2
              • .NET Version: .NET 9.0
              Describe the bug
              
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
              To Reproduce
              
Steps to reproduce the behavior:
              1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
              2. Set all non-FastForest trainers to false (such that only FastForest remains)
              3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
              4. Compare the best model to the reported parameters
              Expected behavior
              
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
              Screenshots, Code, Sample Projects
              Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
              
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
              Image
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                  None yet
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                  No branches or pull requests
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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
                  FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
                  Closed
                  Closed
                  FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
                  Description
                  @JoshuaSloan
                  JoshuaSloan
                  opened on Sep 9, 2025
                  System Information (please complete the following information):
                  • OS & Version: Windows 11
                  • ML.NET Version: ML.NET v4.0.2
                  • .NET Version: .NET 9.0
                  Describe the bug
                  
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
                  To Reproduce
                  
Steps to reproduce the behavior:
                  1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
                  2. Set all non-FastForest trainers to false (such that only FastForest remains)
                  3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
                  4. Compare the best model to the reported parameters
                  Expected behavior
                  
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
                  Screenshots, Code, Sample Projects
                  Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
                  
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
                  Image
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                  Metadata
                  Assignees
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                      None yet
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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
                      FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
                      Closed
                      Closed
                      FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
                      Description
                      @JoshuaSloan
                      JoshuaSloan
                      opened on Sep 9, 2025
                      System Information (please complete the following information):
                      • OS & Version: Windows 11
                      • ML.NET Version: ML.NET v4.0.2
                      • .NET Version: .NET 9.0
                      Describe the bug
                      
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
                      To Reproduce
                      
Steps to reproduce the behavior:
                      1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
                      2. Set all non-FastForest trainers to false (such that only FastForest remains)
                      3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
                      4. Compare the best model to the reported parameters
                      Expected behavior
                      
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
                      Screenshots, Code, Sample Projects
                      Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
                      
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
                          Closed
                          Closed
                          FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
                          Description
                          @JoshuaSloan
                          JoshuaSloan
                          opened on Sep 9, 2025
                          System Information (please complete the following information):
                          • OS & Version: Windows 11
                          • ML.NET Version: ML.NET v4.0.2
                          • .NET Version: .NET 9.0
                          Describe the bug
                          
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
                          To Reproduce
                          
Steps to reproduce the behavior:
                          1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
                          2. Set all non-FastForest trainers to false (such that only FastForest remains)
                          3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
                          4. Compare the best model to the reported parameters
                          Expected behavior
                          
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
                          Screenshots, Code, Sample Projects
                          Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
                          
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
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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
                              FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline #7498
                              Closed
                              Closed
                              FastForest NumberOfLeaves hyperparameter not updating in Regression/MulticlassClassification SweepablePipeline#7498
                              Description
                              @JoshuaSloan
                              JoshuaSloan
                              opened on Sep 9, 2025
                              System Information (please complete the following information):
                              • OS & Version: Windows 11
                              • ML.NET Version: ML.NET v4.0.2
                              • .NET Version: .NET 9.0
                              Describe the bug
                              
FastForestRegression and FastForestOva (unlike FastForestBinary) do not modify the NumberOfLeaves hyperparameter despite being defined in the associated search space. Consequently, while performance for BinaryClassification is on par with comparable AutoML frameworks (e.g. FLAML using exclusively RandomForest), it falls behind for the other task types.
                              To Reproduce
                              
Steps to reproduce the behavior:
                              1. Train an AutoML experiment using a SweepablePipeline for either Regression or MulticlassClassification
                              2. Set all non-FastForest trainers to false (such that only FastForest remains)
                              3. Append an experiment monitor and log the TrialResult.TrialSettings.Parameters
                              4. Compare the best model to the reported parameters
                              Expected behavior
                              
I would expect the model hyperparameters logged from the experiment monitor's final ReportBestTrial() method to match the best model obtained from the experiment. However, while other model hyperparameters are in alignment, the NumberOfLeaves looks suspiciously like the default FastTree value.
                              Screenshots, Code, Sample Projects
                              Trial 31 obtained new best model with (loss: 63689.36950302901, metric: 63689.36950302901)
                              
{"pipeline":{"SCHEMA":"e0 * e2 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":15,"NumberOfLeaves":81,"FeatureFraction":0.8326445,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}},"SCHEMA":"e0 * e1 * e3 * e4","e0":{"OutputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"],"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income"]},"e1":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e2":{"OutputColumnNames":["ocean_proximity"],"InputColumnNames":["ocean_proximity"]},"e3":{"InputColumnNames":["longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","ocean_proximity"],"OutputColumnName":"Features"},"e4":{"NumberOfTrees":100,"NumberOfLeaves":100,"FeatureFraction":1,"LabelColumnName":"median_house_value","FeatureColumnName":"Features"}}
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