fit

Builtin index · Statistics & ML

result = pipe.fit(X, y)

Input: X, y · Output: result

What it is

Runs a pipeline Record pipe (from pipeline(...)) on an N-row feature matrix X and a length-N target vector y: applies every transform stage in order, then calls the final estimator stage.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = pipe.fit(X, y)`	`X` Record, matrix, or vectornot documented yet `y` Record, matrix, or vectornot documented yet	`result` Record	Returns a fitted pipeline Record whose `.predict`/`.score` replay the same transform stages before delegating to the fitted model inside.

Examples

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

centre(X) := X - mean(X)
final(X, y) := ridge_model(X, y, 0.1)

pipe = pipeline("centre", "final")
fitted = pipe.fit(X, y)
print("pipeline R^2 {fitted.score(X, y):.4f}")

output

pipeline R^2 0.9992

from specialized

standardize_cols(M) := (M - mean(M)) ./ std(M)
final(A, b) := ridge_model(A, b, 0.1)

pipe = pipeline("standardize_cols", "final")
fitted = pipe.fit(split.X_train, split.y_train)
print("held-out R2: {round(fitted.score(split.X_test, split.y_test), 4)}")

output

held-out R2: 0.991

from statistics-ml

How it works

fit(pipelineSpec, X, y) — callable as pipe.fit(X, y). Runs every transform stage in order (each stage's output feeds the next), then calls the final stage with (transformed_X, y) and wraps whatever Model it returns — .predict/.score on the *fitted* pipeline replay the same transform stages before delegating to that wrapped model.

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