{% if results_eval is defined and results_model is defined %}
Metric Weighted Micro (Global average) Macro (Class-based average)
Precision {{ results_model.precision_weighted|safe }} {{ results_model.precision_micro|safe }} {{ results_model.precision_macro|safe }}
Recall {{ results_model.recall_weighted|safe }} {{ results_model.recall_micro|safe }} {{ results_model.recall_macro|safe }}
F1 Score {{ results_model.fmeasure_weighted|safe }} {{ results_model.fmeasure_micro|safe }} {{ results_model.fmeasure_macro|safe }}

10-fold Cross Validation Accuracy (+/- Standard Deviation)

Uses 10 consecutive folds to split the data.

{{ results_eval.accuracy|safe }} (+/- {{ results_eval.std|safe }})

Cohen's Kappa

Gives a better indicator of how the classifier performed across all instances. Accuracy can be skewed if the class distribution is similarly skewed.

{{ results_model.cohens_kappa|safe }}

{% endif %}

Confusion Matrix

{% for index in range(0, results_model.genre_names|length) %} {{ results_model.confusion_matrix[0:][index] | sum }} instances for {{ results_model.genre_names[index] }}
{% endfor %}


{% if plot_div is defined %}
{{ plot_div|safe }}
{% endif %}