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- This entry has heavy ties to Data-Processing.
- A method mentioned here is Dimensionality Reduction
6.5 Unsupervised Dimensionality Reduction
✒️ -> Usage:
It can be helpful when number of features is high. This can make the data more manageable.
6.5.1 - PCA: Principal Component Analysis
decomposition.PCA - Looks for a linear combination of features that captures as much variance as possible
6.5.2 - Random Projections
No clue.
More expanded on in 6.6
6.5.3 - Feature agglomeration
Applies Hierarchical clustering to group together features that behave similarly.
Feature scaling
Note that if features have very different scaling or statistical properties, cluster.FeatureAgglomeration may not be able to capture the links between related features. Using a preprocessing.StandardScaler can be useful in these settings.