Answer / Veeresh Kumar Shakya

Principal Component Analysis (PCA) is a technique used in data analysis to reduce the dimensionality of data while retaining most of its important features.nIt works by finding the principal components, or directions of maximum variance in the data, and projecting the data onto these new axes. This can make the data easier to visualize and analyze without losing too much information.

Is naive bayes a supervised or unsupervised method?

1 Answers  

---

Is machine learning a good career?

1 Answers  

---

Explain Principal Component Analysis (PCA)?

1 Answers  

---

Explain the function of Supervised Learning?

1 Answers  

---

How would you use f1 score?

1 Answers  

---

Why machine learning?

1 Answers  

---

What is backpropagation in machine learning?

1 Answers  

---

Tell us when will you use classification over regression?

1 Answers  

---

What is adagrad algorithm in machine learning?

1 Answers  

---

Why do we convert categorical variables into factor?

1 Answers  

---

Mention key business metrics that help ML?

1 Answers  

---

How to choose notable variables while working on a data set?

1 Answers  

---