[source] # Principal Component Analysis Principal Component Analysis (PCA) is a dimensionality reduction technique that aims to transform the feature space by the *k* principal components that explain the most variance. PCA is used to compress high-dimensional samples down to lower dimensions such that they would retain as much information as possible. **Interfaces:** [Transformer](api.md#transformer), [Stateful](api.md#stateful), [Persistable](../persistable.md) **Data Type Compatibility:** Continuous only ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | dimensions | | int | The target number of dimensions to project onto. | ## Example ```php use Rubix\ML\Transformers\PrincipalComponentAnalysis; $transformer = new PrincipalComponentAnalysis(15); ``` ## Additional Methods Return the proportion of information lost due to the transformation: ```php public lossiness() : ?float ``` ## References [^1]: H. Abdi et al. (2010). Principal Component Analysis.