Answer / 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.

Is naïve bayes a supervised or unsupervised method?

1 Answers  

---

What were the last machine learning papers you read? Why do you think the was important?

1 Answers  

---

What is the difference between machine learning vs data mining?

1 Answers  

---

Which language is better for machine learning r or python?

1 Answers  

---

In what areas Pattern Recognition is used?

1 Answers  

---

Do you have research experience in machine learning?

0 Answers  

---

How do we know which machine learning algorithm is better for us to solve our problem?

1 Answers  

---

What is decision tree classification?

1 Answers  

---

Explain how can we use your machine learning skills to generate revenue?

1 Answers  

---

Explain why is naive bayes better than decision tree?

1 Answers  

---

What is the baseline in machine learning?

1 Answers  

---

How will you explain machine learning into a layperson?

1 Answers  

---