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I am studying machine learning, and I have encountered the concept of **bias** and **variance**. I am a university student and in the slides of my professor, the **bias** is defined as:

$bias = E[error_s(h)]-error_d(h)$

where $h$ is the hypotesis and $error_s(h)$ is the **sample error** and $error_d(h)$ is the **true error**. In particular, it says that we have bias when the training set and the test set are not independent.

After reading this, I was try to get a little deepr in the concept, so I searched on internet and found this video , where it defines the bias as **the impossibility to capture the true relationship by a machine learning momdel**.

I don't understand, **are the two definition equal or the two type of bias are different?**

together with this, I am also studying the concept of **variance**, and in the slides of my professor it is said that if I consider two different samples from the sample error may vary even if the model is **unbiased**, but in the video I posted it says that the variance is the **difference in fits between training set and test set**.

Also in this case the definitions are different, **why?**

1What exactly do the bulleyes and the points on the bulleye diagram represent? I get that it's (probably) the error of individual predictions, but isn't that usually one-dimensional? Or is it just an abstract picture intended to convey an idea rather than being a concrete representation of some data? – NotThatGuy – 2020-08-12T22:18:44.950

The bullseys are hypothetical Loss functions, with the center being the global minimum to be reached. But you can think of them as

actualbullseys, and the points being shots. – Leevo – 2020-08-13T14:02:47.720