Overcoming overfitting and underfitting
Choosing the right complexity for your model is a delicate balancing act. If your model is too complex, it might overfit the training data, meaning it performs well on the training data but poorly on new, unseen data. On the other hand, if your model is too simple, it might underfit the data, missing important patterns and leading to inaccurate predictions.
Imagine you’re a market researcher trying to predict consumer trends. An overfitted model might capture every minor fluctuation in past trends but fail to generalize to future trends. An underfitted model might miss important trends altogether.
Navigating training-serving skew and model drift
In an ideal world, your model would perform just as well in the real world as it does on your training data. But this is rarely the case. This discrepancy is known as training-serving skew.
Furthermore, as the underlying data changes over time, your model’s performance can degrade...
