Answer / Deepanshu Jetley

To avoid overfitting, techniques like cross-validation, regularization (L1 or L2), early stopping, and dropout can be used. Cross-validation helps assess the generalization performance of a model by evaluating it on multiple subsets of the data. Regularization adds a penalty term to the loss function to discourage complex models with too many parameters. Early stopping stops training once the validation error starts increasing, indicating overfitting.

What's the “kernel trick” and how is it useful?

1 Answers  

---

Is python good for machine learning?

1 Answers  

---

Describe precision and recall?

1 Answers  

---

Explain when should you use classification over regression?

1 Answers  

---

How much data will you allocate for your training, validation and test sets?

1 Answers  

---

What is the difference between inductive and deductive learning?

1 Answers  

---

What are the different Algorithm techniques in Machine Learning?

1 Answers  

---

Explain the Algorithm of Neural Networks (back propagation) in Machine Learning?

1 Answers  

---

How is ML different from artificial intelligence?

1 Answers  

---

What value do you optimize when using a support vector machine (SVM)?

1 Answers  

---

Explain the difference between bayesian and frequentist?

1 Answers  

---

How does deductive and inductive machine learning differ?

1 Answers  

---