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| <title>SAP RPT-1 OSS Benchmarking — Home</title> | |
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| <!-- ░░ HERO ░░ --> | |
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| <div class="hero-badge" style="margin-bottom: 2rem; display: inline-block;">🚀 The Ultimate Benchmark</div> | |
| <h1 class="landing-title">Compare Models.<br><span class="gradient-text">Instantly.</span></h1> | |
| <p class="landing-subtitle"> | |
| Upload your CSV and let our automated arena pit the world's best ML models against each other. | |
| Discover whether SAP RPT-1 OSS or traditional gradient boosters win on your specific data. | |
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| <a href="/static/uploader.html" class="btn-cta">Enter the Arena ⚔️</a> | |
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| <p style="color: #64748b; font-weight: 600; text-transform: uppercase; letter-spacing: 0.1em; margin-bottom: 1.5rem; font-size: 0.85rem;">Supported Models</p> | |
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| <div class="model-item">XGBoost</div> | |
| <div class="model-item">LightGBM</div> | |
| <div class="model-item">CatBoost</div> | |
| <div class="model-item">TabPFN</div> | |
| <div class="model-item">SAP RPT-1 OSS</div> | |
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| <!-- ░░ HOW IT WORKS ░░ --> | |
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| <div class="hero-badge" style="margin-bottom: 1rem;">PIPELINE</div> | |
| <h2 class="landing-title" style="font-size: 3rem; margin-bottom: 1rem;">How it <span class="gradient-text">Works</span></h2> | |
| <p class="landing-subtitle">From raw CSV to actionable model recommendation in minutes — fully automated.</p> | |
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| <div class="step-icon">📤</div> | |
| <div class="step-num">01</div> | |
| <h4>Upload CSV</h4> | |
| <p>Drag & drop your dataset. We auto-detect features, types, and whether it's a classification or regression task.</p> | |
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| <div class="workflow-arrow">→</div> | |
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| <div class="step-icon">🏋️</div> | |
| <div class="step-num">02</div> | |
| <h4>Parallel Training</h4> | |
| <p>All 5 models run 5-fold cross-validation simultaneously. XGBoost, LightGBM, CatBoost, TabPFN & SAP RPT-1.</p> | |
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| <div class="workflow-arrow">→</div> | |
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| <div class="step-icon">🧩</div> | |
| <div class="step-num">03</div> | |
| <h4>Ensemble Engine</h4> | |
| <p>The top 3 models are automatically combined via Soft Voting and Stacking to squeeze out extra performance.</p> | |
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| <div class="workflow-arrow">→</div> | |
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| <div class="step-icon">🔬</div> | |
| <div class="step-num">04</div> | |
| <h4>SHAP Analysis</h4> | |
| <p>The winner is retrained on the full dataset. SHAP values reveal exactly which features matter most.</p> | |
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| <div class="workflow-arrow">→</div> | |
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| <div class="step-icon">🎮</div> | |
| <div class="step-num">05</div> | |
| <h4>Live Playground</h4> | |
| <p>Tweak feature values in real-time and see your model's live prediction update instantly.</p> | |
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| <!-- ░░ FEATURES ░░ --> | |
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| <div class="feature-icon">⚡</div> | |
| <h3>Zero Configuration</h3> | |
| <p>Simply drag and drop your CSV file. We automatically detect your target variable, infer the task type (classification or regression), and handle preprocessing.</p> | |
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| <div class="feature-card"> | |
| <div class="feature-icon">🔍</div> | |
| <h3>Rigorous Validation</h3> | |
| <p>All models are evaluated using 5-fold cross-validation to ensure statistically significant and reliable results, preventing overfitting on small datasets.</p> | |
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| <div class="feature-card"> | |
| <div class="feature-icon">🧠</div> | |
| <h3>Ensemble Insights</h3> | |
| <p>We don't just pick a winner. We automatically build Voting and Stacking ensembles to see if combining the models yields even better performance.</p> | |
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