Detailed analysis of synthetic data generation results
This report presents a detailed analysis of the synthetic data generated using the {{ generator_info.method }} method. The synthetic dataset contains {{ generator_info.num_samples }} samples and aims to preserve the statistical properties of the original dataset while maintaining privacy.
Generation Method: {{ generator_info.method }}
Number of Samples: {{ generator_info.num_samples }}
Random Seed: {{ generator_info.random_state }}
Overall Quality Score: {{ "%.2f"|format(quality_score) }}
Statistical Similarity: {{ "%.2f"|format(metrics.overall.statistical_similarity) }}
Privacy Score: {{ "%.2f"|format(metrics.overall.privacy_score) }}
Real Data Size: {{ metrics.overall.real_data_size }}
Synthetic Data Size: {{ metrics.overall.synthetic_data_size }}
Size Ratio: {{ "%.2f"|format(metrics.overall.size_ratio) }}
Category | Metric | Value | Interpretation |
---|---|---|---|
{{ category|capitalize }} | {{ metric_name|replace('_', ' ')|capitalize }} | {{ "%.4f"|format(metric_value) if metric_value is number else metric_value }} | {% if category == 'statistical' and metric_name in ['avg_ks_statistic', 'avg_distribution_difference', 'correlation_mean_difference'] %} {% if metric_value < 0.1 %} Excellent {% elif metric_value < 0.2 %} Good {% elif metric_value < 0.3 %} Acceptable {% else %} Poor {% endif %} {% elif category == 'privacy' and metric_name == 'at_risk_percentage' %} {% if metric_value < 1 %} Very Low Risk {% elif metric_value < 5 %} Low Risk {% elif metric_value < 10 %} Moderate Risk {% else %} High Risk {% endif %} {% elif category == 'utility' and 'score' in metric_name %} {% if metric_value > 0.9 %} Excellent {% elif metric_value > 0.8 %} Good {% elif metric_value > 0.7 %} Acceptable {% else %} Poor {% endif %} {% else %} - {% endif %} |
{{ col }} | {% endfor %}
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{{ value }} | {% endfor %}
{{ col }} | {% endfor %}
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{{ value }} | {% endfor %}