<model_name>!SHOW_THRESHOLD_METRICS¶

Returns raw counts and metrics for a specific threshold for each class in models where evaluation was enabled at instantiation. This method takes no arguments. See Metrics in show_threshold_metrics.

Output¶

Column	Type	Description
`dataset_type`	VARCHAR	The name of the dataset used for metrics calculation, currently EVAL.
`class`	VARCHAR	The predicted class. Each class has its own set of metrics, which are provided in multiple rows.
`threshold`	FLOAT	Threshold used to generate predictions.
`precision`	FLOAT	Precision for the given class. The ratio of true positives to the total predicted positives.
`recall`	FLOAT	Recall for the given class. Also called “sensitivity.” The ratio of true positives to the total actual positives.
`f1`	FLOAT	F1 score for the given class.
`tpr`	FLOAT	True positive rate for the given class.
`fpr`	FLOAT	False positive rate for the given class.
`tp`	INTEGER	Total count of true positives in the given class.
`fp`	INTEGER	Total count of false positives in the given class.
`tn`	INTEGER	Total count of true negatives in the given class.
`fn`	INTEGER	Total count of false negatives in the given class.
`accuracy`	FLOAT	The accuracy (ratio of correct predictions, both positive and negative, to the total number of predictions) for the given class.
`support`	INTEGER	The support (true positives plus false negatives) for the given class.
`logs`	VARIANT	Contains error or warning messages.