pca_model

Builtin index · Statistics & ML

result = pca_model(X, k)

Input: X, k · Output: result

What it is

PCA as a reusable handle: fits the top k principal components on an N-row, D-column matrix X, so .predict(Xnew) projects new rows onto the same fitted components instead of refitting.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = pca_model(X, k)`	`X` matrixnot documented yet `k` matrixnot documented yet	`result` Record or matrix	Returns a fitted model Record whose `.predict` gives an `(M, k)` matrix for an `M`-row `Xnew`.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

r = ridge_model(X, y, 0.1)           # least squares with L2 regularization

k = kmeans_model(X, 2, seed = 42)    # .predict assigns to the nearest centroid

p = pca_model(X, 1)                  # .predict projects onto the fitted components

print("cluster of row 0: {k.predict(X)[0]}")

output

cluster of row 0: 1

from specialized

Gs = vstack(randn(20, 2, seed = 64) + 4, randn(20, 2, seed = 65) - 4)
pm = pca_model(Gs, 1)
print("pca score is summed explained variance: {round(pm.score(Gs), 4)}")

output

pca score is summed explained variance: 0.9807

from statistics-ml

How it works

pca_model(X, k) — wraps pca_fit's components/mean so .predict(Xnew) (project new rows onto the same k components) works on data the model wasn't fit on, unlike the flat pca/pca_components.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.