JuniorTheoryCommon

What does this program print?

Python

from sklearn.datasets import load_iris
from sklearn.decomposition import PCA

X, y = load_iris(return_X_y=True)
pca = PCA(n_components=2)
X_new = pca.fit_transform(X)
print(X_new.shape, pca.components_.shape)

(150, 2) (2, 4)

Expecting components_ of shape (4, 2): it holds one row per component and one column per original feature, so its shape is (n_components, n_features).
Expecting X_new to keep 4 columns: fit_transform returns the projections, one column per kept component, so it has 2 columns.

How is one row of X_new computed from a row of X and components_?
What shape would components_ have without n_components?

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JuniorTheoryCommon

What does this program print?

Python

from sklearn.datasets import load_iris
from sklearn.decomposition import PCA

X, y = load_iris(return_X_y=True)
pca = PCA(n_components=2)
X_new = pca.fit_transform(X)
print(X_new.shape, pca.components_.shape)

(150, 2) (2, 4)

Expecting components_ of shape (4, 2): it holds one row per component and one column per original feature, so its shape is (n_components, n_features).
Expecting X_new to keep 4 columns: fit_transform returns the projections, one column per kept component, so it has 2 columns.

How is one row of X_new computed from a row of X and components_?
What shape would components_ have without n_components?

Back to questions Open topic in roadmap →Answer it yourself — Quiz