What is variance in Data Science?

Variance is a sort of inaccuracy that happens when a Data Science model becomes too complicated and learns characteristics from data while also taking into account noise. Even though the data and underlying patterns and trends are relatively straightforward to uncover, this type of inaccuracy can arise if the technique used to train the model is sophisticated. As a result, the model is extremely sensitive, performing well on the training dataset but poorly on the testing dataset and on any type of data it hasn’t seen before. Variance leads to poor testing accuracy and overfitting in most cases.