Performance metrics machine learning

1. What are different performance metrics in machine learning?
2. What is performance matrix in machine learning?
3. How do you measure machine learning performance?
4. What are the performance metrics for classification?

What are different performance metrics in machine learning?

We can use classification performance metrics such as Log-Loss, Accuracy, AUC(Area under Curve) etc. Another example of metric for evaluation of machine learning algorithms is precision, recall, which can be used for sorting algorithms primarily used by search engines.

What is performance matrix in machine learning?

Performance metrics are a part of every machine learning pipeline. They tell you if you're making progress, and put a number on it. All machine learning models, whether it's linear regression, or a SOTA technique like BERT, need a metric to judge performance.

How do you measure machine learning performance?

Various ways to evaluate a machine learning model's performance

1. Confusion matrix.
2. Accuracy.
3. Precision.
4. Recall.
5. Specificity.
6. F1 score.
7. Precision-Recall or PR curve.
8. ROC (Receiver Operating Characteristics) curve.

What are the performance metrics for classification?

The most commonly used Performance metrics for classification problem are as follows, Accuracy. Confusion Matrix. Precision, Recall, and F1 score.