Answer / Lalit Kumar

Kernel Support Vector Machines (SVMs) are a popular machine learning algorithm used for classification and regression problems. They work by finding the optimal hyperplane that separates data points of different classes in high-dimensional feature spaces. When the original data cannot be linearly separated, SVMs use a kernel function to map the data into a higher dimensional space where it can be separated more easily. Common kernels include linear, polynomial, radial basis function (RBF), and sigmoid.

Explain the difference between machine learning and regression?

1 Answers  

---

What cross-validation technique would you use on a time series dataset?

1 Answers  

---

What is reasoning and its types in artificial intelligence?

1 Answers  

---

What are the classification problems in machine learning?

1 Answers  

---

Tell us do you have research experience in machine learning?

0 Answers  

---

What is svm in machine learning? What are the classification methods that svm can handle?

1 Answers  

---

Do you know what is kernel svm?

1 Answers  

---

When is ridge regression favorable over lasso regression?

1 Answers  

---

What is the Model Building in Machine Learning?

1 Answers  

---

What is the “curse of dimensionality?

1 Answers  

---

What are the two paradigms of ensemble methods?

1 Answers  

---

Tell us what are some differences between a linked list and an array?

1 Answers  

---