Answer Posted / Harish Kumar Malik

Precision and recall are two important metrics for evaluating classification models. Precision is the proportion of true positives (correctly classified positive instances) among all predicted positives, while recall is the proportion of true positives among all actual positives in the data. In other words, precision measures how many of the predicted positives were actually correct, and recall measures how many of the actual positives were correctly identified.

Do you have research experience in machine learning?

154

Tell us do you have research experience in machine learning?

224

Tell me what are the last machine learning papers you've read?

292

What are standardization and normalisation? Give one advantage of each over the other?

149