feat: documents pca

src/decomposition/mod.rs

@@ -1 +1,15 @@
+//! # Dimension Reduction Methods
+//! Dimension reduction is a popular approach for deriving a low-dimensional set of features from a large set of variables.
+//!
+//! High Dimensional Data (a lot of input features) often degrade performance of machine learning algorithms due to [curse of dimensionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality).
+//! The more dimensions you have in a data set, the more difficult it becomes to predict certain quantities. While it seems that the more explanatory variables the better,
+//! when it comes to adding variables, the opposite is true. Each added variable results in an exponential decrease in predictive power.
+//! Therefore, it is often desirable to reduce the number of input features.
+//!
+//! Dimension reduction is also used for the purposes of data visualization.
+//!
+//! ## References
+//! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., 10.3.1 K-Means Clustering, 6.3 Dimension Reduction Methods](http://faculty.marshall.usc.edu/gareth-james/ISL/)
+
+/// PCA is a popular approach for deriving a low-dimensional set of features from a large set of variables.
 pub mod pca;