Why underfitting is called high bias and overfitting is called high variance?
How can one understand it intuitively?


Underfitting is called "Simplifying assumption" (Model is HIGHLY BIASED towards its assumption). your model will think linear hyperplane is good enough to classify your data which may not be true. consider you are shown a picture of cat 1000 times, Now you are blindfolded, No matter Whatever you are shown the 1001th time, probability that you will say cat is very high(You are HIGHLY BIASED that the next picture is also gonna be a cat). Its because you believe its gonna be a cat anyway. Here you are simplifying assumptions

In statistics, Variance informally means how far your data is spread out.
Overfitting is you memorise 10 qns for your exam and on the next day exam, only one question has been asked in the question paper from that 10 you read. Now you will answer that one qn correctly just like in the book, but you have no idea what the remaining questions are(Question are HIGHLY VARIED from what you read). In overfitting, model will memorise the entire train data such that it will give high accuracy on train but will suck in test. Hope its helps