verticapy.machine_learning.memmodel.decomposition.PCA.transform#
- PCA.transform(X: list | ndarray) ndarray #
Transforms and applies the
PCA
model to the input matrix.Parameters#
- X: ArrayLike
The data on which to make the transformation.
Returns#
- numpy.array
Transformed values.
Examples#
Import the required module.
from verticapy.machine_learning.memmodel.decomposition import PCA
We will use the following attributes:
principal_components = [ [0.4, 0.5], [0.3, 0.2], ] mean = [0.1, 0.3]
Let’s create a model.
model_pca = PCA(principal_components, mean)
Create a dataset.
data = [[4, 5]]
Transform the data.
model_pca.transform(data) Out[6]: array([[2.97, 2.89]])
Note
Refer to
PCA
for more information about the different methods and usages.