{% block content %}
Field Summary
  1. Inferred variable type: during event creation in Amazon Fraud Detector, you will need to map variables in your data to a list of predefined variable types. You should first try to select variable types to the best of your knowledge. If you cannot find the matching variable types or are unsure about the variable types of your variables, you can use the Inferred Variable Type column below as reference.
  2. Count: the total number of records (rows) in your data.
  3. # Distinct: the number of unique values in this feature column.
  4. % Distinct: the ratio between # Distinct and Count.
  5. # Missing: the number of missing values in this feature column.
  6. % Missing: the ratio between # Missing and Count.
{% for ix, rec in df_stats.iterrows() %} {% endfor %}
Name Data Type Inferred Variable Type Count # Distinct % Distinct # Missing % Missing
{{ rec['_column'] }} {{ rec['_dtype'] }} {{ rec['_feature'] }} {{ rec['count'] }} {{ rec['nunique'] }} {{ '{0: >#016.2f}'.format(rec['nunique_pct']*100) }}% {{ rec['null'] }} {{ '{0: >#016.2f}'.format(rec['null_pct']*100)}}%
{% if warnings.shape[0] > 0 %}
Field Warnings
The following {{warnings.shape[0]}} fields may cause potential issues, check the message and consider excluding them from model training.
{% for ix, rec in warnings.iterrows() %} {% endfor %}
Name Data Type Inferred Variable Type Count # Distinct % Distinct # Missing % Missing Message
{{ rec['_column'] }} {{ rec['_dtype'] }} {{ rec['_feature'] }} {{ rec['count'] }} {{ rec['nunique'] }} {{ '{0: >#016.2f}'.format(rec['nunique_pct']*100) }}% {{ rec['null'] }} {{ '{0: >#016.2f}'.format(rec['null_pct']*100)}}% {{ rec['_message'] }}
{% endif %} {% endblock %}