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Balancing Bias And Variance

Bias-Variance Dilemma

What is bias-variance tradeoff?

This question is frequently asked in machine learning interviews. Although it is an entry-level question, you can demonstrate your understanding of machine learning by explaining the answer beautifully. Because you can not write a code or draw bullseye diagrams during a virtual video call, you need to explain the fundamentals of the bias-variance tradeoff in simple sentences.

Error In Machine Learning Models

There are two sources of error that prevent a machine learning algorithm from generalizing: Bias and variance. The bias-variance tradeoff is the problem of minimizing two sources of error at the same time.

Bias

Bias is the difference between the predicted value and the actual value of the target. Bias occurs due to the assumptions of the model. Bias can be thought of as an error in the test data.

Variance

Variance is a value that shows how far is the data spread out. It is a measure of variability in the model. It shows how similar your bias error is…