Comment on page
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.
There are two ways to access. One is to access from 'Add' (Plus) button. 
Another way is to access from a column header menu. 
- 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.
Last modified 2yr ago