National Institute of Technology PHD Researcher Interview Questions and Answers

Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.

• Type I error is also known as a false positive.

• Type II error is also known as a false negative.

• Type I error occurs when the researcher concludes that there is a significant effect when there is not.

• Type II error occurs when the researcher concludes that there is no significant effect when there actually is...read more

Regularization is a technique used to prevent overfitting in machine learning models.

• Regularization adds a penalty term to the loss function to discourage complex models.

• It helps to reduce the variance of the model and improve its generalization performance.

• L1 and L2 regularization are commonly used techniques.

• L1 regularization adds the absolute value of the coefficients to the loss function.

• L2 regularization adds the ...read more