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.