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What causes high BIAS/VARIANCE and what are the consequences. Can someone explain in simple terms w.r.t to training/testing errors?

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It Bias and Variance Tradeoff which most ML models try to optimise.

Bias : High Bias classifier learned on the training set is not expressive enough to even account for the data provided. In this case, both the training error and the test error will be high, as the classifier does not account for relevant information present in the training set.

Variance : The classifier learned on the training set is too specific, and cannot be used to accurately infer anything about unseen data. Although training error continues to decrease over time, test error will begin to increase again as the classifier begins to make decisions based on patterns which exist only in the training set and not in the broader distribution.

Please refer to image below for more details :

enter image description here

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