Answer / Priya Yadav

F1 score is the harmonic mean of precision and recall, and it provides a balanced evaluation metric for classification problems. It is calculated by taking the average of precision and recall weighted by their reciprocals. F1 score ranges from 0 to 1, with higher values indicating better performance. We can use F1 score to compare multiple classifiers or to evaluate the performance of a single classifier on multiple classes.

What do you understand by reinforcement learning technique?

1 Answers  

---

What are the 3 types of ai?

1 Answers  

---

How do you think google is training data for self-driving cars?

1 Answers  

---

What should I learn before machine learning?

1 Answers  

---

How will you choose the most appropriate machine learning algorithm for your classification problem?

1 Answers  

---

What is data augmentation in ml?

1 Answers  

---

What are the smaller dataset techniques?

1 Answers  

---

What is the use of gradient descent?

1 Answers  

---

What is a Confusion Matrix?

1 Answers  

---

If a highly positively skewed variable has missing values and we replace them with mean, do we underestimate or overestimate the values?

1 Answers  

---

Who invented machine learning?

1 Answers  

---

Tell us how would you approach the “netflix prize” competition?

1 Answers  

---