When a model makes a probability estimation, the differences between the model’s estimation values and existing values arise. And the significant difference is identified as bias. Bias is the inability of machine learning algorithms like Linear Regression to understand the true relationship between data points. If alternative training data were used, the variance would delineate the proficiency of variation in the estimation. In commoner’s terms, variance delineates how distant a random variation degenerates from its estimated value.