ML.DIM_REDUCTION.KERNEL_PCA¶
Creates a Kernel Principal Component Analysis object.
Syntax¶
Arguments¶
| Name | Type | Default | Description |
|---|---|---|---|
| n_components | int | None | Number of components to keep. If None, all components are kept. |
| kernel | str | "linear" | Kernel type to use in the algorithm. One of: 'linear', 'poly', 'rbf', 'sigmoid', 'cosine'. |
| degree | int | 3 | Degree for the polynomial kernel. Ignored by all other kernels. |
| gamma | float | 1.0 | Kernel coefficient for 'rbf', 'poly', and 'sigmoid'. If gamma is 'auto', then 1/n_features will be used. |
| coef0 | float | 0.0 | Independent term in kernel function. It is only significant in 'poly' and 'sigmoid' kernels. |
Examples¶
Examples coming soon
Working Excel formula examples for this function are not yet written.