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<title>SAP RPT-1 OSS Benchmarking — Home</title>
<meta name="description" content="Discover the ultimate ML model arena. Benchmark XGBoost, LightGBM, CatBoost, TabPFN, and SAP RPT-1 OSS in seconds."/>
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<a href="/static/landing.html" class="nav-brand" id="nav-logo">ModelMatrix</a>
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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>
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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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<!-- ░░ MODELS BANNER ░░ -->
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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-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-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="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="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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<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-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-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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