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Bias and Variance

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Dr. Dinesh K Vishwakarma

PROFESSOR, DEPARTMENT OF INFORMATION TECHNOLOGY

DELHI TECHNOLOGICAL UNIVERSITY, DELHI.

Webpage: http://www.dtu.ac.in/Web/Departments/InformationTechnology/faculty/dkvishwakarma.php

Email: dinesh@dtu.ac.in

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What is Bias?

    • Bias is the difference between the average prediction of our model and the correct value which we are trying to predict.
    • Model with high bias pays very little attention to the training data and oversimplifies the model. It always leads to high error on training and test data.

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What is variance?

    • Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.
    • Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but has high error rates on test data.

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Fundamentals of Bias and Varience

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Bias and variance using bulls-eye diagram

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Overfitting

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Why is Bias Variance Tradeoff?

  • If our model is too simple and has very few parameters then it may have high bias and low variance.
  • If our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data.
  • This tradeoff in complexity is why there is a tradeoff between bias and variance. An algorithm can’t be more complex and less complex at the same time.

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References

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