Answer / Km.nicky

Model building is the process of creating a mathematical or computational model that can accurately predict outputs given inputs. In machine learning, this involves selecting an appropriate algorithm, training the model on a dataset, evaluating its performance, and fine-tuning it to improve its accuracy. Model building often requires careful selection of input features, handling of missing data, and regularization techniques to prevent overfitting. Cross-validation is commonly used for evaluating the performance of models.

How is machine learning used in day-to-day life?

1 Answers  

---

Do you have research experience in machine learning?

0 Answers  

---

What is semi-supervised Machine Learning?

1 Answers  

---

Explain the difference between type I and type ii error?

1 Answers  

---

What is keras sequential model?

1 Answers  

---

How do classification and regression differ?

1 Answers  

---

Why is “naive” bayes naive?

1 Answers  

---

Tell us when should you use classification over regression?

1 Answers  

---

List down various approaches for machine learning?

1 Answers  

---

What is candidate sampling in machine learning?

1 Answers  

---

Why naive bayes is called naive?

1 Answers  

---

Mention the difference between Data Mining and Machine learning?

1 Answers  

---