#0188

Remove Leakage Features

MediumML25 min20 XP

Problem

Given feature_names and unavailable_names, return feature names that are safe to use.

Why This Matters

Leakage can make a model look excellent offline and fail in production. Feature availability is one of the first checks in real ML work.

Function Signature

def safe_features(feature_names, unavailable_names):

Examples

Example 1

Inputfeatures = ['age','future_payment','city'], unavailable = ['future_payment']

Output['age', 'city']

future_payment is removed because it would not be known at prediction time.

Constraints

Return the exact requested result.
Handle common edge cases.
Keep the solution readable before optimizing.

CodePython

def safe_features(feature_names, unavailable_names):
    """
    Input example: features = ['age','future_payment','city'], unavailable = ['future_payment']
    Return: ['age', 'city']
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return []

def safe_features(feature_names, unavailable_names):
    """
    Input example: features = ['age','future_payment','city'], unavailable = ['future_payment']
    Return: ['age', 'city']
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return []

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Core example

Inputfeatures = ['age','future_payment','city'], unavailable = ['future_payment']

Expected['age', 'city']

future_payment is removed because it would not be known at prediction time.

Hidden Test Categories

minimal inputrepeated valueslarger realistic input