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Interesting approach for drift detection! Can you please tell me if the partition summary in the case of embeddings is the same as below (https://dm4ml.github.io/gate/how-it-works/) or are you taking into account other factors:
coverage: The fraction of the column that has non-null values.
mean: The mean of the column.
p50: The median of the column.
num_unique_values: The number of unique values in the column.
occurrence_ratio: The count of the most frequent value divided by the total count.
p95: The 95th percentile of the column.
The text was updated successfully, but these errors were encountered:
Interesting approach for drift detection! Can you please tell me if the partition summary in the case of embeddings is the same as below (https://dm4ml.github.io/gate/how-it-works/) or are you taking into account other factors:
coverage: The fraction of the column that has non-null values.
mean: The mean of the column.
p50: The median of the column.
num_unique_values: The number of unique values in the column.
occurrence_ratio: The count of the most frequent value divided by the total count.
p95: The 95th percentile of the column.
The text was updated successfully, but these errors were encountered: