Is precision and recall metrics are good for imbalanced class problems?

Is precision and recall metrics are good for imbalanced class problems?

In this case, the focus on the minority class makes the Precision-Recall AUC more useful for imbalanced classification problems. There are other ranking metrics that are less widely used, such as modification to the ROC Curve for imbalanced classification and cost curves.

Is precision and recall good for Imbalanced data?

If you have an imbalanced dataset accuracy can give you false assumptions regarding the classifier’s performance, it’s better to rely on precision and recall, in the same way a Precision-Recall curve is better to calibrate the probability threshold in an imbalanced class scenario as a ROC curve.

Why accuracy is not a good metric for very imbalanced data?

… in the framework of imbalanced data-sets, accuracy is no longer a proper measure, since it does not distinguish between the numbers of correctly classified examples of different classes. Hence, it may lead to erroneous conclusions …

Why should we use precision and recall as our metrics for this problem rather than the usual ROC metrics?

Use ROC when the positives are the majority or switch the labels and use precision and recall — When the positive class is larger we should probably use the ROC metrics because the precision and recall would reflect mostly the ability of prediction of the positive class and not the negative class which will naturally …

Is AUC good for Imbalanced Data?

ROC AUC and Precision-Recall AUC provide scores that summarize the curves and can be used to compare classifiers. ROC Curves and ROC AUC can be optimistic on severely imbalanced classification problems with few samples of the minority class.

Is Precision good for imbalanced class problems?

Precision metric tells us how many predicted samples are relevant i.e. our mistakes into classifying sample as a correct one if it’s not true. this metric is a good choice for the imbalanced classification scenario.

What are good precision and recall values?

In information retrieval, a perfect precision score of 1.0 means that every result retrieved by a search was relevant (but says nothing about whether all relevant documents were retrieved) whereas a perfect recall score of 1.0 means that all relevant documents were retrieved by the search (but says nothing about how …

What is good AUC PR?

A random estimator would have a PR-AUC of 0.09 in your case (9\% positive outcomes), so your 0.49 is definitely a substantial increase. If this is a good result could only be assessed in compariso to other algorithms, but you didn’t give detail on the method/data you used.

Is AUC good for Imbalanced data?

Is accuracy metric is a good idea for imbalanced class problems?

Accuracy metric is not a good idea for imbalanced class problems.

Is recall more important than precision?

Precision is more important than recall when you would like to have less False Positives in trade off to have more False Negatives. Meaning, getting a False Positive is very costly, and a False Negative is not as much.

Why is precision/recall so important for AUC?

With imbalanced classes, it’s easy to get a high AUC without actually making useful predictions, so looking at precision/recall helps you analyze how well you’re predicting each class. You have to use these metrics together, and it might also be useful to bring in the f1 score, which combines precision and recall for both of the classes.

Is accurate accurately calculated AUC enough?

Accuracy alone is not sufficient to prove that you are obtaining good results. Moreover, there is the receiver-operator curve (ROC). This will tell you your false positive rate for any true positive rate. You can then calculate the area under this curve (AUC) to get a comparable metric of performance.

What are the ROC curves and precision-recall curves?

ROC Curves and Precision-Recall Curves provide a diagnostic tool for binary classification models. ROC AUC and Precision-Recall AUC provide scores that summarize the curves and can be used to compare classifiers. ROC Curves and ROC AUC can be optimistic on severely imbalanced classification problems with few samples of the minority class.

What is the difference between precision and recall?

The result is a value between 0.0 for no precision and 1.0 for full or perfect precision. Recall is a metric that quantifies the number of correct positive predictions made out of all positive predictions that could have been made.