Detailed analysis of the optimal student model
This report presents a detailed analysis of the best performing distilled (student) model. The {{ model.model_type }} model was trained using knowledge distillation with a temperature of {{ model.temperature }} and alpha of {{ model.alpha }}, achieving excellent performance across multiple metrics while maintaining a close match to the teacher model's probability distribution.
Model Type: {{ model.model_type }}
Temperature: {{ model.temperature }}
Alpha: {{ model.alpha }}
Accuracy: {{ "%.3f"|format(metrics.accuracy.value) }} {% if metrics.accuracy.retention is defined %}({{ "%.1f"|format(metrics.accuracy.retention) }}% of teacher){% endif %}
F1 Score: {{ "%.3f"|format(metrics.f1.value) if 'f1' in metrics else 'N/A' }} {% if 'f1' in metrics and metrics.f1.retention is defined %}({{ "%.1f"|format(metrics.f1.retention) }}% of teacher){% endif %}
AUC-ROC: {{ "%.3f"|format(metrics.auc_roc.value) if 'auc_roc' in metrics else 'N/A' }} {% if 'auc_roc' in metrics and metrics.auc_roc.retention is defined %}({{ "%.1f"|format(metrics.auc_roc.retention) }}% of teacher){% endif %}
KL Divergence: {{ "%.3f"|format(metrics.kl_divergence.value) if 'kl_divergence' in metrics else 'N/A' }}
KS Statistic: {{ "%.3f"|format(metrics.ks_statistic.value) if 'ks_statistic' in metrics else 'N/A' }}
R² Score: {{ "%.3f"|format(metrics.r2_score.value) if 'r2_score' in metrics else 'N/A' }}
A straight diagonal line would indicate identical distributions. {% if 'r2_score' in metrics %}The R² score of {{ "%.3f"|format(metrics.r2_score.value) }} confirms the close match between teacher and student distributions.{% endif %}
Metric | Teacher Model | Student Model | Difference | Retention % |
---|---|---|---|---|
{{ metric.display_name }}{% if metric_name in ['kl_divergence', 'ks_statistic'] %} (lower is better){% endif %} | {{ "%.3f"|format(metric.teacher_value) if metric.teacher_value is not none else 'N/A' }} | {{ "%.3f"|format(metric.value) }} | {% if metric.difference is defined and metric.teacher_value is not none %} {% if metric_name in ['kl_divergence', 'ks_statistic'] %} {{ "+%.3f"|format(metric.difference) if metric.difference > 0 else "%.3f"|format(metric.difference) }} {% else %} {{ "%.3f"|format(metric.difference) }} {% endif %} {% else %} N/A {% endif %} | {% if metric.retention is defined %} {{ "%.1f"|format(metric.retention) }}% {% elif metric_name in ['kl_divergence', 'ks_statistic'] %} N/A {% else %} N/A {% endif %} |
Top features by importance in the {{ model.model_type }} model. Feature importance represents the relative contribution of each feature to the model's predictions.