Answer / Abhijit Kumar Sinha

Overfitting occurs when a model learns the training data too well, including its noise and outliers. This results in a model that performs well on the training set but poorly on new, unseen data (underfits or generalizes poorly). To avoid overfitting, techniques like cross-validation, regularization (L1 and L2), early stopping, and dropout can be used.

Tell us what's the difference between type I and type ii error?

1 Answers  

---

Describe precision and recall?

1 Answers  

---

What are the most important machine learning techniques?

1 Answers  

---

Which is more important to you – model accuracy or model performance?

1 Answers  

---

An example where ensemble techniques might be useful?

1 Answers  

---

What is the difference between type I and type ii error?

1 Answers  

---

What is your training in machine learning and what types of hands-on experience do you have?

1 Answers  

---

What do you understand by Eigenvectors and Eigenvalues?

1 Answers  

---

What are the different Algorithm techniques in Machine Learning?

1 Answers  

---

Why is python best for machine learning?

1 Answers  

---

Which language is better for machine learning?

1 Answers  

---

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

1 Answers  

---