{ "evaluation_id": "EVAL-20260402-184305", "question_id": "ANALYSIS-002", "question_text": "You receive this dataset summary for a machine learning project predicting customer churn:\n\n```\nTotal records: 50,000\nFeatures: 45\nTarget: churned (1) vs retained (0)\nClass balance: 5% churned, 95% retained\n\nSample statistics:\n- age: mean=34.2, std=12.1, min=-5, max=150\n- account_balance: mean=$2,340, std=$15,000, 5000 missing values\n- last_login: 30% missing, format varies (MM/DD/YYYY and YYYY-MM-DD mixed)\n- customer_id: unique count = 48,500 (out of 50,000 records)\n- country: 200 unique values including \"USA\", \"usa\", \"United States\", \"US\"\n\nCorrelation with target:\n- age: 0.02\n- account_balance: 0.45\n- days_since_last_login: 0.67\n- total_purchases: 0.52\n```\n\nWhat data quality issues do you identify? What would you do before training a model?", "category": "analysis", "timestamp": "2026-01-21T00:00:00.000Z", "display_date": "Jan 21, 2026", "winner": { "name": "Claude Sonnet 4.6", "provider": "openrouter", "score": 9.59 }, "avg_score": 8.769, "matrix_size": 87, "models_used": [ { "id": "gpt_oss_120b", "name": "GPT-OSS-120B", "provider": "OpenAI" }, { "id": "claude_opus_46", "name": "Claude Opus 4.6", "provider": "openrouter" }, { "id": "gpt_5_4", "name": "GPT-5.4", "provider": "openrouter" }, { "id": "deepseek_v4", "name": "DeepSeek V4", "provider": "openrouter" }, { "id": "mimo_v2_flash", "name": "MiMo-V2-Flash", "provider": "Xiaomi" }, { "id": "claude_sonnet_46", "name": "Claude Sonnet 4.6", "provider": "openrouter" }, { "id": "grok_420", "name": "Grok 4.20", "provider": "openrouter" }, { "id": "gemini_3_flash", "name": "Gemini 3 Flash Preview", "provider": "Google" }, { "id": "minimax_m25", "name": "MiniMax M2.5", "provider": "openrouter" }, { "id": "gemini_31_pro", "name": "Gemini 3.1 Pro", "provider": "openrouter" } ], "rankings": { "claude_sonnet_46": { "display_name": "Claude Sonnet 4.6", "provider": "openrouter", "average_score": 9.59, "score_count": 9, "min_score": 9, "max_score": 10, "rank": 1 }, "gpt_oss_120b": { "display_name": "GPT-OSS-120B", "provider": "OpenAI", "average_score": 9.49, "score_count": 9, "min_score": 8.8, "max_score": 10, "rank": 2 }, "gpt_5_4": { "display_name": "GPT-5.4", "provider": "openrouter", "average_score": 9.48, "score_count": 9, "min_score": 9, "max_score": 10, "rank": 3 }, "grok_420": { "display_name": "Grok 4.20", "provider": "openrouter", "average_score": 9.34, "score_count": 8, "min_score": 8.6, "max_score": 9.8, "rank": 4 }, "mimo_v2_flash": { "display_name": "MiMo-V2-Flash", "provider": "Xiaomi", "average_score": 9.23, "score_count": 8, "min_score": 8.55, "max_score": 10, "rank": 5 }, "gemini_3_flash": { "display_name": "Gemini 3 Flash Preview", "provider": "Google", "average_score": 9.17, "score_count": 8, "min_score": 8.8, "max_score": 10, "rank": 6 }, "deepseek_v4": { "display_name": "DeepSeek V4", "provider": "openrouter", "average_score": 8.87, "score_count": 9, "min_score": 8, "max_score": 9.8, "rank": 7 }, "claude_opus_46": { "display_name": "Claude Opus 4.6", "provider": "openrouter", "average_score": 8.83, "score_count": 9, "min_score": 7.6, "max_score": 9.6, "rank": 8 }, "gemini_31_pro": { "display_name": "Gemini 3.1 Pro", "provider": "openrouter", "average_score": 7.7, "score_count": 9, "min_score": 6.3, "max_score": 9.2, "rank": 9 }, "minimax_m25": { "display_name": "MiniMax M2.5", "provider": "openrouter", "average_score": 5.99, "score_count": 9, "min_score": 3.3, "max_score": 8.6, "rank": 10 } }, "judgments": [ { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.35, "brief_justification": "Highly accurate, thorough, and practical. It identifies the key data quality issues and gives a strong preprocessing roadmap, with only minor overreach in a few speculative points and implementation details." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.4, "brief_justification": "The response correctly identifies all data quality issues and provides excellent, deep insights with highly actionable code snippets. However, the response is cut off at the very end, slightly impacting completeness and clarity." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "The response flawlessly identifies all data quality issues, provides comprehensive and highly actionable preprocessing steps, and demonstrates deep ML expertise by highlighting risks like data leakage and grouped splitting for non-unique IDs." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9.8, "brief_justification": "The response accurately identifies all data quality issues and provides a comprehensive, well-structured, and highly actionable set of preprocessing steps." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.55, "brief_justification": "" }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "The response is exceptionally accurate, comprehensive, and well-structured. It provides deep insights such as identifying MNAR data and potential target leakage, along with highly actionable code snippets and a practical checklist." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.8, "brief_justification": "The response is exceptionally well-structured, accurately identifying all data quality issues and providing a comprehensive, actionable, and insightful step-by-step plan for preprocessing and modeling." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.75, "brief_justification": "The response perfectly identifies all data quality issues, provides actionable code snippets, and demonstrates deep understanding of ML pitfalls like target leakage and pipeline data leakage. Minor note: log1p on account balances might fail if balances are negative, but overall excellent." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 10, "brief_justification": "The response perfectly identifies all data quality issues and provides a highly comprehensive, accurate, and actionable pre-processing plan with excellent depth." }, { "judge_key": "gemini_31_pro", "judge_name": "Gemini 3.1 Pro", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 3.9, "brief_justification": "The response is cut off mid-sentence, contains a translation glitch (Chinese characters), misses several key data issues like class imbalance, and completely fails to answer the second half of the prompt regarding actionable steps." }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 7.65, "brief_justification": "The response correctly identifies all major data quality issues: class imbalance, duplicate records, invalid/outlier values, missing data, inconsistent formatting, and begins to mention data leakage. The analysis is well-structured and clearly explained. However, the response is clearly truncated—it cuts off mid-sentence during the data leakage discussion and never delivers the promised 'Part 2' with pre-training steps. This significantly impacts completeness and usefulness, as the actionable re" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough, correctly identifying all major data quality issues (invalid ages, missing data, inconsistent formats, duplicate IDs, categorical inconsistencies, class imbalance, outliers/skew, and feature leakage risk). The preprocessing recommendations are comprehensive, well-organized, and actionable, covering everything from validation strategy to encoding to threshold tuning. The prioritization at the end adds practical value. Depth is excellent with nuanced points " }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.8, "brief_justification": "The response accurately identifies all major data quality issues (class imbalance, missing values, invalid ages, duplicate IDs, inconsistent formats, high cardinality) and provides well-structured, actionable preprocessing steps. It correctly notes the skewed distribution of account_balance, recommends appropriate techniques (SMOTE, median imputation with missing indicators, stratified CV, proper metrics), and includes important considerations like temporal leakage and multicollinearity. Minor g" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.2, "brief_justification": "The response is highly comprehensive, correctly identifying all major data quality issues (class imbalance, invalid age values, high variance in account_balance, missing data, inconsistent formatting, duplicate IDs) and providing detailed, actionable preprocessing steps. The structure is excellent with clear headings and a summary pipeline. Minor nitpicks: the mention of 'negative correlation' as a potential issue is slightly forced, and the response could have mentioned checking whether missing" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "This is an exceptional response. Every data quality issue is correctly identified with precise reasoning. The duplicate analysis correctly flags data leakage risk. The MNAR insight for missing last_login is sophisticated and important. The ordered pipeline (duplicates first, split before imputation, missingness indicators before imputing) demonstrates deep practical knowledge. The final point about potential temporal leakage in days_since_last_login (0.67 correlation) is an expert-level observat" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.55, "brief_justification": "The response is exceptionally thorough, correctly identifying all major data quality issues (class imbalance, invalid values, duplicates, missing data, inconsistent categoricals, mixed date formats, potential leakage). The phased remediation plan is well-structured, practical, and actionable, covering cleaning, imputation, feature engineering, imbalance handling, and pre-modeling steps. Minor nitpick: age capping at 18 minimum is an assumption that may not hold for all businesses, and the respon" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough, correctly identifying all major data quality issues (class imbalance, invalid age values, missing values, inconsistent date formats, country spelling variants, duplicate customer IDs, potential target leakage, skewed distributions, and unknown remaining features). It goes beyond identification to provide a comprehensive, step-by-step pre-modeling roadmap with concrete code examples, rationale for each decision, and practical implementation guidance. The le" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.2, "brief_justification": "The response correctly identifies all major data quality issues (class imbalance, duplicates, impossible values, missing data, inconsistent formatting, potential leakage) and provides a comprehensive, well-structured action plan. The leakage discussion around days_since_last_login is insightful and practically important. The phased approach is clear and actionable. Minor deductions: could have mentioned the low correlation of age (0.02) as a potential candidate for removal, and could have discus" }, { "judge_key": "claude_opus_46", "judge_name": "Claude Opus 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 4.95, "brief_justification": "The response correctly identifies several key data quality issues (impossible age values, missing data, inconsistent date formats, duplicate IDs) but is clearly truncated/incomplete. It cuts off mid-sentence in the table and never addresses the severe class imbalance (5%/95%), inconsistent country naming conventions, high cardinality of country, the pre-training steps (feature engineering, handling imbalance with SMOTE/undersampling, encoding strategies, train/test splitting considerations, etc." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 6.3, "brief_justification": "Identifies several key issues correctly—class imbalance, missing data, invalid values, duplicate/customer-level ambiguity, and inconsistent formatting—but the response is truncated before finishing, so it does not fully answer what should be done before training. It also overstates duplicates from customer_id uniqueness, since repeated IDs may be valid multiple records rather than true duplicates." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 7.6, "brief_justification": "Strong identification of key issues like invalid values, missingness, duplicates, inconsistent categories, imbalance, and leakage risk. Clear and practical overall, but the response is truncated, includes some questionable recommendations (e.g., clipping age, KNN imputation choice, assuming duplicate count equals duplicate records), and misses some essentials like split-before-resampling/imputation and stronger discussion of evaluation/calibration and timestamp reference handling." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8, "brief_justification": "Accurately identifies most key issues: missingness, inconsistent categories/date formats, invalid ages, likely duplicate IDs, skew/outliers, and class imbalance. Recommendations are clear and practical. Slightly overstates some points (e.g., country high cardinality is less severe than ID leakage/duplication concerns) and misses a few nuances such as treating customer_id as a likely non-predictive identifier to drop, checking target leakage around engineered days_since_last_login, and investigat" }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.55, "brief_justification": "Strong, well-structured answer that identifies most key issues and gives actionable preprocessing steps. Minor drawbacks: it treats duplicate customer IDs as definite duplicates without considering repeated customers over time, and the 'negative correlation' point is unnecessary/confused since the reported correlation is positive. Overall very practical and thorough." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.15, "brief_justification": "Accurate and very actionable response that identifies the major data quality issues, covers leakage risk, preprocessing order, missingness, duplicates, categorical/date cleanup, and imbalance handling with practical steps. Minor deduction because it assumes duplicate customer_id necessarily indicates a data quality issue and suggests age bounds that may be domain-specific." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.8, "brief_justification": "Accurate and well-structured identification of key issues: invalid values, missingness, mixed date formats, categorical inconsistencies, duplicate IDs, class imbalance, and dropping customer_id. Practical preprocessing steps are strong. Minor deductions because class imbalance is more a modeling challenge than a pure data quality issue, and the duplicate-record inference from customer_id uniqueness is plausible but not guaranteed without checking repeated IDs versus missing/ID anomalies." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Accurately identifies major issues like imbalance, missingness, invalid values, inconsistent formats, duplicate IDs, and potential leakage, and gives a strong actionable preprocessing plan. Minor limitations: it assumes duplicate customer_id implies duplicate records, does not explicitly mention checking whether customer_id should be dropped as an identifier feature, and could discuss missingness mechanisms or temporal split considerations more explicitly." }, { "judge_key": "gpt_5_4", "judge_name": "GPT-5.4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 3.3, "brief_justification": "Identifies some real issues like invalid ages, missing data, and inconsistent date formats, but the response is truncated, incomplete, and leaves out several important issues and preprocessing steps such as class imbalance, inconsistent country categories, potential duplicate records, target-aware leakage checks, and concrete actions before modeling." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.6, "brief_justification": "Comprehensive and accurate identification of issues with clear actionable steps." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.8, "brief_justification": "Comprehensive and detailed response addressing all data quality issues with actionable solutions." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.6, "brief_justification": "Comprehensive and accurate identification of data issues with actionable preprocessing steps." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.6, "brief_justification": "Comprehensive and accurate identification of issues with actionable preprocessing steps." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.8, "brief_justification": "Comprehensive and accurate analysis with actionable steps." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.6, "brief_justification": "Comprehensive and accurate identification of data issues with actionable steps for preprocessing and modeling." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.8, "brief_justification": "Comprehensive, accurate, and actionable response with detailed steps and clear explanations." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.6, "brief_justification": "Comprehensive and accurate identification of issues with actionable pre-processing steps." }, { "judge_key": "deepseek_v4", "judge_name": "DeepSeek V4", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 8.6, "brief_justification": "Accurate identification of issues with clear explanations and actionable insights." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 9.2, "brief_justification": "Excellent identification of critical issues including class imbalance, duplicates, outliers, missing data, formatting inconsistencies, and data leakage risk. The response is structured, actionable, and demonstrates deep understanding of ML data preparation." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.35, "brief_justification": "Comprehensive, accurate, and actionable with excellent structure and depth." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.35, "brief_justification": "Comprehensive, accurate, and highly actionable response covering all major data quality issues and preprocessing steps with clear prioritization." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9, "brief_justification": "Accurate identification of issues and comprehensive preprocessing steps, well-structured and actionable." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.35, "brief_justification": "Excellent analysis covering all major issues (duplicates, impossible values, missing data patterns, inconsistency, imbalance) with actionable code snippets and critical warnings about data leakage and MNAR patterns." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.35, "brief_justification": "Comprehensive, accurate, and highly actionable with clear structure and practical steps." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 9.35, "brief_justification": "Comprehensive and accurate identification of issues with a detailed, actionable plan; clear structure and high practical value." }, { "judge_key": "mimo_v2_flash", "judge_name": "MiMo-V2-Flash", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7.6, "brief_justification": "Accurate identification of key issues, but response is incomplete and cuts off mid-sentence." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 7.8, "brief_justification": "The response correctly identifies all major data quality issues including class imbalance, duplicates, invalid values, missing data, inconsistent formatting, and begins to address data leakage. The structure is clear and well-organized. However, the response appears to be cut off mid-sentence on the data leakage point, which is a significant omission since leakage (days_since_last_login correlation of 0.67) is a critical concern. The response also lacks the 'what to do before training' action pl" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.35, "brief_justification": "Exceptionally thorough response covering all major data quality issues: class imbalance, invalid age values, missing data with missingness-as-feature insight, date format inconsistency, duplicate records, and categorical encoding problems. The data leakage warning about days_since_last_login is a sophisticated and often-overlooked insight. Code examples are practical and correct. Minor deduction for correctness/completeness as the response appears truncated at the end (the pipeline section cuts " }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.35, "brief_justification": "Exceptionally thorough response that correctly identifies all major data quality issues including the subtle ones (leakage risk, duplicate customer IDs, ambiguous date parsing). The preprocessing steps are well-prioritized and actionable. The practical checklist and prioritization at the end add significant value. Minor deduction on correctness only because the response could have noted that negative account balances might be legitimate (overdrafts) rather than errors, showing slightly less doma" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.6, "brief_justification": "The response accurately identifies all major data quality issues from the dataset summary and provides actionable preprocessing steps. It correctly flags class imbalance, missing values, invalid age values, duplicate customer IDs, inconsistent formats, and high cardinality. The preprocessing recommendations are practical and well-organized. Minor gaps include not explicitly discussing leakage risks from customer_id duplicates (could be same customer appearing twice), not deeply exploring the acc" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 9.35, "brief_justification": "Excellent, comprehensive response that correctly identifies all major data quality issues including class imbalance, outliers, missing data, formatting inconsistencies, and duplicate IDs. The preprocessing pipeline is well-structured, technically sound, and actionable. Minor note: the 'negative correlation' section is slightly off-base as a 'data quality issue' since 0.67 positive correlation with days_since_last_login is expected and logical, but the verification suggestion is reasonable. The r" }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.3, "brief_justification": "The response identifies all major data quality issues correctly and provides insightful analysis (e.g., the duplicate ID issue and data leakage risk). However, the response is incomplete as it cuts off mid-sentence at the end of Part 1, failing to provide the 'step-by-step actions' promised in the introduction." }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.8, "brief_justification": "Excellent response that correctly identifies all major data quality issues including class imbalance, invalid values, duplicates, missing data, inconsistent categoricals, and format issues. The phased action plan is practical and well-structured. Minor gap: doesn't explicitly discuss the low correlation of age (0.02) suggesting it may be a weak feature worth dropping, and could mention data leakage risks more thoroughly. The recommendation to use stratified splits and proper pipeline constructio" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9.55, "brief_justification": "Exceptionally thorough response that correctly identifies all major data quality issues (class imbalance, invalid age values, missing data, date format inconsistencies, country normalization, duplicate IDs, potential leakage, skewed distributions). The response goes well beyond surface-level identification with actionable code examples, decision rationale, and a structured pipeline approach. The leakage concern about high-correlation features is particularly insightful. Minor deduction on depth " }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Excellent response that correctly identifies all major data quality issues including class imbalance, duplicates, impossible values, missing data, formatting inconsistencies, and potential data leakage. The action plan is well-structured across logical phases with specific, actionable recommendations. The leakage concern about days_since_last_login is a sophisticated insight. Minor gap: doesn't explicitly mention train/test split should happen before imputation to prevent data leakage from imput" }, { "judge_key": "claude_sonnet_46", "judge_name": "Claude Sonnet 4.6", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 4.7, "brief_justification": "The response correctly identifies several data quality issues (impossible age values, missing data, inconsistent date formats, duplicate IDs) but appears to be truncated mid-sentence, cutting off before completing the analysis. It's missing discussion of class imbalance (5%/95% split), country normalization issues ('USA'/'usa'/'United States'/'US'), the high std on account_balance suggesting outliers, and critically lacks the second half of the question about what to do before training. The tabl" }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 8.6, "brief_justification": "The response accurately identifies all major data quality issues from the summary (imbalance, duplicates, outliers, missing values, inconsistent formatting, high cardinality) with clear explanations. It structures the answer logically into issues and planned actions (though the provided text cuts off mid-sentence). High clarity and usefulness for a ML project." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.8, "brief_justification": "Comprehensive identification of all major data quality issues with specific, actionable code examples and sound ML practices. Minor incompleteness as the final pipeline is cut off." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9, "brief_justification": "Comprehensive identification of all major data quality issues with thorough, well-structured preprocessing recommendations that cover leakage risks, practical steps, and modeling considerations." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.6, "brief_justification": "The response accurately identifies all major data quality issues from the summary (invalid age, missing values, date/country inconsistencies, duplicates, imbalance) with appropriate preprocessing recommendations. It is clearly structured, covers the topic thoroughly with actionable steps, and provides useful ML-specific advice like SMOTE, stratified CV, and feature engineering. Minor deduction for completeness as it doesn't explicitly mention checking for data leakage in the customer_id duplicat" }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.8, "brief_justification": "The response comprehensively and accurately identifies all major data quality issues from the summary (imbalance, outliers/invalid values, missing data, inconsistencies, duplicates) with clear risks explained. Preprocessing steps are thorough, practical, and well-structured with appropriate techniques (e.g., median imputation for skewed data, SMOTE/class weights, proper metrics). Minor note on correlation sign interpretation is slightly speculative but overall insightful and actionable." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9, "brief_justification": "Comprehensive identification of all major data quality issues with insightful analysis (e.g., MNAR missingness, leakage risks). Provides actionable, well-structured steps with code examples and proper ML practices like splitting before imputation." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 9, "brief_justification": "The response comprehensively identifies all major data quality issues from the summary with excellent structure, actionable code examples, and practical modeling advice while correctly addressing leakage risks and imbalance." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Comprehensive identification of all major data quality issues with accurate, actionable preprocessing steps. Minor note that missing values for account_balance are actually 10% (5000/50000), not exactly as phrased, but overall excellent." }, { "judge_key": "grok_420", "judge_name": "Grok 4.20", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7.85, "brief_justification": "The response accurately identifies key data quality issues from the summary (outliers in age, missing values, inconsistent formats, duplicates) with clear explanations of why they matter. It is well-structured in a table format. However, it is incomplete as it cuts off mid-sentence at 'customer_id has' and does not address all issues like country value inconsistencies, class imbalance, or provide the 'what would you do' actions before training a model as asked in the question." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 6.3, "brief_justification": "Identifies key quality problems accurately, but the answer is incomplete and lacks a full set of preprocessing steps, reducing completeness and practical usefulness." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 8.6, "brief_justification": "Accurately identifies key data issues, offers thorough and well-structured remediation steps, and provides actionable code and deeper considerations like leakage and non-linear effects." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9.25, "brief_justification": "Accurate identification of issues, thorough coverage, clear structure, insightful considerations like leakage and grouping, and actionable preprocessing steps." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.6, "brief_justification": "Accurately identifies key data issues and offers concrete, actionable preprocessing steps with clear organization." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 8.8, "brief_justification": "Accurately identifies key data quality problems and offers detailed, actionable preprocessing steps; well‑structured and clear, with strong practical guidance." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.2, "brief_justification": "Accurately identifies data issues, offers thorough preprocessing steps, clear structure, insightful analysis of missingness and leakage, and actionable code and metric guidance." }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 8.6, "brief_justification": "" }, { "judge_key": "gpt_oss_120b", "judge_name": "GPT-OSS-120B", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 5.5, "brief_justification": "Identifies key issues correctly but answer is cut off and omits several important data problems and preprocessing steps, limiting completeness and practical value." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9.6, "brief_justification": "The response is exceptionally thorough, identifying all data quality issues (imbalance, invalid ages, missing values, inconsistent formats, duplicates, and categorical noise). It provides actionable Python code for each issue and correctly identifies potential data leakage risks." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 10, "brief_justification": "The response is exceptionally thorough, identifying all subtle data quality issues (like the customer_id discrepancy implying duplicates) and providing a comprehensive, professional-grade preprocessing roadmap." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 9.8, "brief_justification": "The response accurately identifies all data quality issues present in the prompt (invalid ages, duplicates, inconsistent formatting, missing values, and class imbalance). The preprocessing steps are logical, comprehensive, and follow industry best practices." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "mimo_v2_flash", "respondent_name": "MiMo-V2-Flash", "weighted_score": 10, "brief_justification": "The response is exceptionally thorough, identifying all data quality issues (outliers, missing values, duplicates, formatting, and imbalance) and providing a logical, step-by-step preprocessing pipeline. It correctly suggests appropriate metrics (F1, PR-AUC) and techniques (SMOTE, log transformation, stratified sampling) for this specific scenario." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 10, "brief_justification": "The response is exceptional. It identifies all data quality issues present in the prompt, provides technical code-based solutions, and offers high-level strategic advice such as preventing data leakage and choosing the correct evaluation metrics for imbalanced classes. The insight regarding MNAR (Missing Not At Random) data for last_login is particularly sophisticated." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.8, "brief_justification": "The response is exceptionally thorough, identifying all data quality issues present in the prompt (invalid ages, duplicates, inconsistent formatting, missing values, and class imbalance). The proposed solutions are industry-standard and logically structured." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 10, "brief_justification": "The response is exceptionally thorough, identifying all data quality issues present in the prompt (including subtle ones like target leakage and non-unique IDs) and providing a comprehensive, actionable roadmap for remediation with code examples." }, { "judge_key": "gemini_3_flash", "judge_name": "Gemini 3 Flash Preview", "respondent_key": "minimax_m25", "respondent_name": "MiniMax M2.5", "weighted_score": 7.55, "brief_justification": "The response identifies the key issues accurately but is incomplete, cutting off mid-sentence and failing to provide the requested 'what would you do' section." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_31_pro", "respondent_name": "Gemini 3.1 Pro", "weighted_score": 6.55, "brief_justification": "Correctly identifies most data quality issues (class imbalance, duplicates, invalid values, missing data, inconsistent formats). However, response is incomplete - cuts off mid-sentence on data leakage, and fails to provide the promised step-by-step actions/fixes. Also misses suspicious high correlation (0.67) on days_since_last_login suggesting potential leakage." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_opus_46", "respondent_name": "Claude Opus 4.6", "weighted_score": 9, "brief_justification": "Comprehensive identification of all major data quality issues (class imbalance, invalid age values, missing data, duplicate IDs, inconsistent country/categorical data, date format issues). Provides technically correct code solutions, discusses important nuances like using PR-AUC over ROC-AUC for imbalanced data, checking for data leakage, and treating missingness as informative. Well-organized with clear sections and actionable pre-training pipeline." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_5_4", "respondent_name": "GPT-5.4", "weighted_score": 9, "brief_justification": "Comprehensive identification of all major data quality issues (invalid ages, missing data, inconsistent dates, duplicate IDs, inconsistent categories, class imbalance, outliers, leakage risk) with thorough, actionable preprocessing steps. Well-organized with clear sections, practical checklist, and prioritization. Goes beyond surface-level to discuss edge cases, specific techniques (SMOTE, stratified splits, grouped splitting), and offers concrete pipeline implementation." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "deepseek_v4", "respondent_name": "DeepSeek V4", "weighted_score": 8.6, "brief_justification": "Comprehensive identification of all major data quality issues with technically sound preprocessing recommendations. Well-organized with specific techniques (SMOTE, stratified k-fold, median imputation). Minor gaps include limited detail on multicollinearity and data leakage investigation." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "claude_sonnet_46", "respondent_name": "Claude Sonnet 4.6", "weighted_score": 9.8, "brief_justification": "Comprehensive identification of all major data quality issues with accurate technical details. Well-structured with clear sections, tables, and code examples. Goes beyond surface-level analysis by explaining underlying mechanisms (MNAR missingness, potential leakage in days_since_last_login). Provides actionable, practical guidance with specific code snippets and a useful checklist. Addresses class imbalance with multiple solutions and emphasizes appropriate metrics for imbalanced data." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "grok_420", "respondent_name": "Grok 4.20", "weighted_score": 9.8, "brief_justification": "Comprehensive identification of all major data quality issues (class imbalance, invalid values, missing data, inconsistent categories, mixed date formats, duplicates) with thorough, actionable preprocessing steps organized in clear phases. Minor deduction for slight vagueness on days_since_last_login feature." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gpt_oss_120b", "respondent_name": "GPT-OSS-120B", "weighted_score": 8.8, "brief_justification": "Comprehensive identification of all major data quality issues with accurate solutions. Well-organized with tables, code snippets, and actionable steps. Covers class imbalance, invalid ages, missing values, date formats, country normalization, duplicates, potential leakage, and outliers. Provides practical preprocessing pipeline with clear explanations of why each step matters." }, { "judge_key": "minimax_m25", "judge_name": "MiniMax M2.5", "respondent_key": "gemini_3_flash", "respondent_name": "Gemini 3 Flash Preview", "weighted_score": 8.8, "brief_justification": "Comprehensive identification of all major data quality issues (class imbalance, duplicates, impossible values, missing data, inconsistent formats, potential leakage). Provides specific, actionable preprocessing steps with appropriate techniques (SMOTE, RobustScaler, StratifiedKFold). Notable insight on data leakage with days_since_last_login. Well-structured and technically sound." } ], "meta": { "source": "The Multivac (app.themultivac.com)", "methodology": "10x10 blind peer matrix evaluation", "criteria": "correctness, completeness, clarity, depth, usefulness", "self_judgments": "excluded from rankings", "license": "Open data — cite as: The Multivac (2026)" } }