Stacking Meta-Features

Problem

Build the meta-feature matrix for stacking by transposing predictions so each sample becomes a row with one column per base model.

Empty input returns [].

Hint

The meta-model learns from out-of-fold base predictions as features.

case 1

in: [[[0.1,0.2,0.3],[0.9,0.8,0.7]]]

expected: [[0.1,0.9],[0.2,0.8],[0.3,0.7]]

case 2

in: [[[1,2]]]

expected: [[1],[2]]

case 3

in: [[]]

expected: []

Starter codePython

def stacking_meta_features(predictions):
    # Your code here
    pass

3 test cases · run your code against them in the editor