There are 2 types of errors: reducible and irreducible. Irreducible errors arise due to a lack of data. For example, not only the genre, duration, actors but also the mood of the person and the atmosphere while watching the film affects the rating. But we can't predict the mood of the person in the future. The other reason is the quality of the data.

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can't get 100% accuracy. And even if we did, the result is still not without errors. There are simply too few test situations to find them. You may ask, what is the matter?

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can't get 100% accuracy. And even if we did, the result is still not without errors. There are simply too few test situations to find them. You may ask, what is the matter?