# Terminology question. Low bias

I understood the meaning of High bias (undefit) and High variance (overfit).

In the course C2W3_Lab_02_Diagnosing_Bias_and_Variance the terminus Low bias is used, in the chapter Fixing high bias, sentence “At this point, you can say that the models with degree greater than 4 are low-bias because they perform close to or better than the baseline.” and further in the text.

This confuses me. Is the Low bias same as High variance?

Thanks

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Would you share with us how you come up with the idea that low bias can be high variance? What logic did you follow to get to that conclusion?

Raymond

Hi @Goran_Hrzenjak good question.

No, low bias and high variance are two different concepts in machine learning.

Bias refers to the difference between the average prediction of a model and the true values of the data. These models tend to have a high error on training and validation sets because they are not flexible enough to fit the complex underlying distribution of the data.

Variance, on the other hand, refers to the variability of a model’s predictions for a given point in the input space. A model with high variance is one that is highly sensitive to small fluctuations in the training data and is able to fit the noise in the data. These models tend to have a low error on the training set but a high error on the validation set.

In the statement you quoted, “the models with degree greater than 4 are low-bias” means that the models are complex enough to fit the data well and thus have low bias. The term low-bias is used to contrast it with a simpler model ( with lower degree of polynomials) that would be of higher bias and not fitting the data as well.

Dear pastorsoto, your answer came as I wrote the text below, so I will send it anyhow, yust to show you my previous conclusions:

In the lection “C2W3 Diagnosing bias and variance” two possible extreme situation are mentioned. High bias (underfit) and High variance (overfit). And in the middle, of course, the “Just right”:

In the training lab "C2W3_Lab_02_Diagnosing_Bias_and_Variance", chapter “Fixing High Bias”, subchapter “Try adding polynomial features”, there is the sentence:

Blockquote
As you can see, the more polynomial features you add, the better the model fits to the training data. In this example, it even performed better than the baseline. At this point, you can say that the models with degree greater than 4 are low-bias because they perform close to or better than the baseline.

Below is the screenshot:

In the same lab, chapter “Fixing High Variance”, subchapter “Try increasing the regularization parameter”, there is the sentence:

Blockquote
In contrast to the last exercise above, setting a very small value of the regularization parameter will keep the model low bias but might not do much to improve the variance. As shown below, you can improve your cross validation error by increasing the value of 𝜆 .

Below is the screenshot:

Looking at the first screenshot I tend to see model well performing on the training set when increasing the degree, but the CV shows increasing of MSE. And that is overfit (High variance), right? In the text the Low bias is mentioned.

The second screenshot is about increasing the value of 𝜆. Increasing the lambda tends to cure the overfit (High variance). Right? The text says “As shown below, you can improve your cross validation error by increasing the value of 𝜆.

So, what does “Low bias” exactly means?
And, is there “Low variance” too, meaning what?

Thanks

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Dear pastorsoto, I thing I got it. Thanks!

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