You won't find a machine learning model that perfectly solves your problems without making even a single mistake, no matter how small. Since every model makes mistakes, it is critical to understand their nature. Suppose that our model makes a prediction and we know the real value. If this prediction is incorrect, then there is some difference between the prediction and the true value:
The other part of this error will come from imperfections in our data, and some from imperfections in our model. No matter how complex our model is, it can only reduce the modeling error. Irreducible error is out of our control, hence its name. ...
