In this recipe, we showed how to create the entire pipeline for a data science project. However, there are many other transformations we can apply to data as preprocessing steps. Some of them include:
- Scaling numerical features: In other words, changing the range of the features due to the fact that different features are measured on different scales; and this can introduce bias to the model. We should mostly be concerned with feature scaling when dealing with models that calculate some kind of distance between features (such as K-Nearest Neighbors). In general, methods based on decision trees do not require any scaling. Some popular options from scikit-learn include StandardScaler and MinMaxScaler.
- Discretizing continuous ...
