What do the ROC Curve and AUC represent in classification problems?
- Curve of false positive rate vs. true positive rate
- Curve of precision vs. recall
- Curve of true negatives vs. false negatives
- nan
The ROC (Receiver Operating Characteristic) Curve is a plot of the false positive rate versus the true positive rate. The AUC (Area Under the Curve) is a single value summarizing the overall ability of the test to discriminate between positive and negative instances.
Loading...
Related Quiz
- Using domain knowledge to transform raw data into a more suitable format or variable for modeling is known as ________.
- The ___________ test in Logistic Regression can be used to assess if the Logit link function is the correct specification for the model.
- In which type of learning do algorithms learn by interacting with an environment to achieve a goal?
- You reduced the complexity of your model to prevent overfitting, but it led to underfitting. How would you find a balance between complexity and fit?
- Can you explain the main concept behind boosting algorithms?