Question - What is variance in Data Science?
Answer -
Variance is a type of error that occurs in a Data Science model when the model ends up being too complex and learns features from data, along with the noise that exists in it. This kind of error can occur if the algorithm used to train the model has high complexity, even though the data and the underlying patterns and trends are quite easy to discover. This makes the model a very sensitive one that performs well on the training dataset but poorly on the testing dataset, and on any kind of data that the model has not yet seen. Variance generally leads to poor accuracy in testing and results in overfitting.