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.

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