Medium

Weighted Gini After Split

~15 mincode completion

Implement weighted_gini(left_counts, right_counts) that returns the weighted Gini of the two children (not the information gain).

Examples

Both pure: weighted Gini = 0

Input
weighted_gini([5, 0], [0, 5])
Output
0

Both 50/50: weighted Gini = 0.5

Input
weighted_gini([2, 2], [3, 3])
Output
0.5

Larger pure child dominates

Input
weighted_gini([8, 0], [1, 1])
Output
0.1

Hints

Hint 1

Sum with , and check which axis you are summing over.

Hint 2

Do not forget to weighting by size. That step is easy to skip.

Requirements

  • left_counts: Class counts for the left child

  • right_counts: Class counts for the right child

  • Return Weighted Gini = (n_L G_L + n_R G_R) / (n_L + n_R)

Constraints

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

Try similar problems(1)
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Where this shows up

~15 min

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Python
import numpy as np

def weighted_gini(left_counts: np.ndarray, right_counts: np.ndarray) -> float:
    """
    Compute the weighted Gini impurity after a binary split.

    Args:
        left_counts:  Class counts for the left child
        right_counts: Class counts for the right child

    Returns:
        Weighted Gini = (n_L * G_L + n_R * G_R) / (n_L + n_R)
    """
    # YOUR CODE HERE
    pass
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