Evaluate Prediction - Binary

Returns a data frame with evaluation score of binary classification including the below.

• AUC

• f_score

• accuracy

• misclassification_rate

• precision

• recall

• specificity

• true_positive - Number of positive predictions that actually are positive.

• false_positive - Number of positive predictions that actually are negative.

• true_negative - Number of negative predictions that actually are negative.

• false_negative - Number of negative predictions that actually are positive.

• test_size - The number of tested data.

• threshold - threshold value for prediction.

How to Access This Feature

From + (plus) Button

There are two ways to access. One is to access from 'Add' (Plus) button.

Another way is to access from a column header menu.

How to Use?

• Predicted Probability Column - The column with predicted values. Usually, it's predicted_probability in the framework of Exploratory.

• Actual Value Column - The column with actual value.

• Threshold Value to Decide Predicted Label - You can choose how to decide threshold for predicted label.

  • Use Optimized Value - This searches threshold to optimize the chosen metric. It can be

    • F Score

    • Accuracy

    • Precision

    • Recall

    • Specificity

  • Enter Manually

    • Set threshold value manually.