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Random Feature Subspace

Implement random feature subspace selection used in Random Forests.

Given a dataset with $m$ features and a list of pre-selected feature indices (to make the output deterministic), return the data with only the selected columns.

In a real Random Forest, $\sqrt{m}$ features are randomly selected at each split. Here, the indices are given.

Return the subsampled feature matrix.

Example:

Input:

X = [[1, 2, 3, 4], [5, 6, 7, 8]]
selected_features = [0, 2]

Output:

[[1, 3], [5, 7]]

Reasoning:

The input dataset X is a 2x4 matrix: [[1, 2, 3, 4], [5, 6, 7, 8]].
The selected_features list contains the indices of the features to be selected: [0, 2].
We select the columns at indices 0 and 2 from the input dataset X, which correspond to the values [1, 3] in the first row and [5, 7] in the second row.
The resulting subsampled feature matrix is a 2x2 matrix: [[1, 3], [5, 7]].

Constraints:

X: 2D list (n_samples x m_features)
selected_features: list of column indices to keep
Return 2D list with only the selected columns, preserving row order

Editor

Python 3.13.1

Test Results

0/0

Run code to see test results.

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