Comprehending prediction errors is crucial when it comes to understanding PREDICTIONS. Reducible (errors that arise due to squared bias or squared variance) and irreducible (errors that arise due to the randomness or natural variability in a system and cannot be reduced by varying the MODEL) mistakes are the two PRIMARY types of errors. There are two types of reducible errors: bias and variance. GAINING a thorough grasp of these flaws aids in the construction of an accurate model by preventing overfitting and underfitting. 

Bias: 

The bias is defined as the difference between the ML model's predicted values and the actual value. Biasing results in a substantial inaccuracy in both training and testing data. To avoid the PROBLEM of underfitting, it is advised that an algorithm be low biassed at all times.

The data predicted is in a straight line format due to significant bias, and hence does not fit accurately in the data set. Underfitting of data is the term for this type of fitting. This occurs when the theory is too straightforward or linear. Consider the graph below as an illustration of a situation like this.

Variance: 

The variance of the model is the variability of model prediction for a given data point, which tells us about the dispersion of our data. It is the difference between the validation error and the training error. The model with high variance has a very complex fit to the training data and so is unable to fit accurately on new data. As a result, while such models perform well on training data, they have high error rates when testing data.

When a model's variance is excessive, it's referred to as Overfitting of Data. Overfitting, which involves accurately fitting the training set using a complicated curve and a high order hypothesis, is not a viable option because the error with unknown data is considerable.

Variance should be kept to a minimum when training a data model.

The model must always aim for a low bias and a low variance in order to achieve the best balance between the two mistakes.