…latively poor than the training score then it’s the problem of over-generalization or over-fitting. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression.
This brings us to a few basic conclusions on optimizing Pandas code:
1. Avoid loops; they’re slow and, in most common use cases, unnecessary.
2. If you must loop, use
apply(), not iteration functions.
3. Vectorization is usually better than scalar operations. Most common operations in Pandas can be vectorized.
4. Vector operations on NumPy arrays are more efficient than on native Pandas series.