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Variance and Bias!

Ever wondered what bias and variance are and how they affect our Machine Learning models? I was on the lookout for the basic definition of Bias and Variance in the ML language. I stumbled upon a site that had a beautiful explanation, and this is what I learned about it.

Bias: Bias is the error that occurs when the model fails to meet the expectations. Say, I have trained a model, and while testing it, I need to measure the accuracy. While doing so, the prediction and testing data are used. When it predicts with an accuracy of 96%, the remaining 4% would be the bias error, error of bias, or simply bias. In order to decrease this error, we should probably introduce variance.

Variance: Variance is the spread of data around the mean point. We can see how it acts when the Machine Learning model changes or becomes sensitive to different datasets apart from the trained values or data.

Now, we need to remember that low bias and high variance can result in overfitting of the model, while the reverse could cause underfitting of the model.

In order to get the right form of the model, we need to use low bias and low variance. There is a method in ML to make this happen. It is called the bias-variance tradeoff. To use this method, we need a technique called Bias Variance Decomposition. Now, this can be used for both Regression as well as Classification models.


As an example taken from geeksforgeeks(.)org



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