Answer / Ashwini Kumar Singh

Missing data in a dataset can be handled using techniques such as imputation (replacing missing values with estimated ones), deletion (removing rows or columns with missing values), and handling them as separate categories. Other methods include regression, mean/median imputation, and multiple imputation.

Why is logistic regression better than naive bayes?

1 Answers  

---

What is sequence model?

1 Answers  

---

Why Accuracy is important in machine learning?

1 Answers  

---

How does a classifier work?

1 Answers  

---

Explain some differences between a linked list and an array?

1 Answers  

---

What is sequence to sequence?

1 Answers  

---

What are standardization and normalisation?

1 Answers  

---

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

1 Answers  

---

Explain the Algorithm of Support vector machines in Machine Learning?

1 Answers  

---

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

1 Answers  

---

Explain the k-nearest algorithm different from the knn clustering?

1 Answers  

---

Define a hash table?

1 Answers  

---