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            Threshold Evaluation for Binary Classification Use Cases

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            Threshold Evaluation for Binary Classification Use Cases

            Threshold Evaluation helps you optimize the threshold value for a model. The threshold value tells your model how to classify a binary outcome. If the calculated probability is above the threshold value, Einstein classifies the outcome one way (such as True or Positive). If the calculated probability is below the threshold value, Einstein classifies the outcome the other way (such as False or Negative).

            Note
            Note Einstein Discovery stories are now models. We wish we could snap our fingers to update the name everywhere, but you can expect to see the previous name in a few places until we replace it.

            Navigate to Threshold Evaluation

            In Performance, click Threshold Evaluation.

            Threshold Evaluation tab, showing the tradeoff between true and false positives

            Metrics on the Threshold Evaluation Tab

            Metric Description
            Controls

            You can set an optimal threshold that represents the cutoff for the two outcomes you are predicting. To change the selected threshold value:

            • Threshold Value: Drag the slider to set the threshold.
            • Optimize a Specific Metric: Select a common metric from the list.
            • Cost Ratio: To let Einstein Discovery pinpoint an optimized threshold, specify a cost ratio (the ratio between the false positives and false negatives).

            The Threshold Value value reflects your selection. In the ROC graph, the blue dot moves to the corresponding location on the Actual Model line that represents the threshold value along the ROC curve.
            ROC Curve

            Receiver Operating Characteristic Curve. Displays the performance measurement at various threshold settings. ROC is a probability curve and AUC (Area Under the Curve) represents the degree or measure of separability. This chart shows how well the model is able to distinguish between classes.

            • Y-axis: True Positive Rate: TPR = TP / (TP + FN)
            • X-axis: False Positive Rate>: FPR = FP / (FP + TN)
            • Model (blue line)
            • No Model (gray line)—the same as random chance
            Number of Rows Number of rows in the training data.
            AUC Area Under the Curve. Represents the rate of correct classification by a logistic model. An AUC of 0.5 means that the model performs no better than random guessing. An AUC of 1.0 means that the model correctly classifies data 100% of the time, which can indicate data leakage.

            See Also

             
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