{{getClusterMeta(cluster_fact.cluster).name}} {{ cluster_fact.size }} ({{(100*cluster_fact.size/facts.global.size).toFixed(2)}}%)
{{getClusterMeta(selectedCluster.cluster).name}}

Observations

  • {{ obs.feature_label }} is in average {{ obs.relative_diff |smartNumber }}% {{obs.polarity}} (in absolute value) : mean of {{obs.mean |smartNumber}} against {{ obs.global_mean |smartNumber }} globally The mean of {{ obs.feature_label }} is {{obs.mean |smartNumber}} against {{ obs.global_mean |smartNumber }} across all clusters {{ polarity == 'smaller' ? 'Only' : ''}} {{ obs.current_ratio * 100 |smartNumber }}% of the cluster has {{ obs.category_value }} for {{ obs.feature_label }} (against {{ obs.global_ratio* 100 |smartNumber }} % globally)
Model
Model ID
Model type
Code Env
Python version
Metadata
Optional. Informative labels for the model. The model:algorithm, model:date, model:name, trainDataset:dataset-name, testDataset:dataset-name labels, evaluation:date and evaluationDataset:dataset-name are automatically added.