Feature Engineering Using The sklearn.preprocessing Package
#machinelearningwithpython #machinelearning #machinelearningalgorithm
When features are in different ranges (e.g., weights and horsepowers), machine learning methods can behave poorly due to the dominance of larger-scaled features.
Techniques to address scale issues:
Standardization: Rescales features to have a mean of 0 and standard deviation of 1. Useful for algorithms assuming Gaussian-like distributions or when feature magnitudes vary greatly.
Normalization: Rescales features to be in the range [0, 1]. Useful for algorithms sensitive to magnitude but not distribution.