Model insight shows feature importance at query/category level and drift detection to understand how ML models make ranking decisions.

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Overview

Model insight helps you understand how machine learning models rank products by showing feature importance and detecting when models drift from expected behavior.

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Feature Importance

{
  "model": "ranking_model_v2",
  "feature_importance": {
    "query_category": "running_shoes",
    "features": [
      {
        "feature": "click_through_rate",
        "importance": 0.35
      },
      {
        "feature": "conversion_rate",
        "importance": 0.30
      },
      {
        "feature": "revenue_per_impression",
        "importance": 0.20
      },
      {
        "feature": "margin",
        "importance": 0.15
      }
    ]
  }
}

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Drift Detection

{
  "drift_detection": {
    "enabled": true,
    "metrics": [
      "feature_distribution",
      "prediction_distribution",
      "performance_metrics"
    ],
    "alert_threshold": 0.1
  }
}

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Best Practices

  1. Monitor regularly: Check feature importance regularly
  2. Detect drift: Set up drift detection alerts
  3. Understand context: Consider query/category context
  4. Retrain when needed: Retrain models when drift detected