Chennaisoft Consultancy Services Interview Questions and Answers

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Q1. What are the difference between Type I and Type II error?

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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.
The probability of Type I error is denoted by alpha, whil...read more

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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 square of the coefficients to the loss function.
The streng...read more