Answer / Neetu Sagar

L1 and L2 regularization are methods used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The main difference between them lies in the nature of this penalty term: L1 regularization applies an absolute value penalty, resulting in sparse solutions with some features having zero weights. On the other hand, L2 regularization uses a squared penalty, which produces smoother models but does not necessarily result in sparse solutions.

What is machine learning in artificial intelligence?

1 Answers  

---

What is motor sequence learning?

1 Answers  

---

Tell me what is precision and recall?

1 Answers  

---

Explain the Algorithm Technique of Transduction in Machine Learning?

1 Answers  

---

Explain Classification and Regression?

1 Answers  

---

What are some methods of reducing dimensionality?

1 Answers  

---

What is the calibration layer in machine learning?

1 Answers  

---

What is Training set in machine learning?

1 Answers  

---

Why naive bayes is called naive?

1 Answers  

---

What is ensemble learning?

1 Answers  

---

Which is the best language for machine learning?

1 Answers  

---

Where do we use encoder and decoder?

1 Answers  

---