"Bias Variance Tradeoff"

Hi @Ahmad_Khalid1

did you check this video already in which Prof. Ng provides a great explanation on this topic:

Which specific part is unclear?

In general it’s about the trade-off between underfitting ans overfitting, see also these threads:

quoting from Wikipedia:

The bias–variance dilemma or bias–variance problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learningalgorithms from generalizing beyond their training set:[1][2]

  • The bias error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).
  • The variance is an error from sensitivity to small fluctuations in the training set. High variance may result from an algorithm modeling the random noise in the training data (overfitting).

Bias–variance tradeoff - Wikipedia

Best regards