#0172

Label Encode Train and Test Safely

MediumML25 min20 XP

Problem

Given train categories and test categories, encode train categories from 0 upward and encode unknown test categories as -1.

Why This Matters

Encoders must be fit on training data only; unseen production categories need a deliberate fallback.

Function Signature

def label_encode_train_test(train_values, test_values):

Examples

Example 1

Inputtrain = ['red','blue','red'], test = ['blue','green']

Output([0, 1, 0], [1, -1])

green was unseen during training, so it becomes -1.

Constraints

Return the exact requested output.
Handle ordinary edge cases.
Prefer simple, interview-readable code.

CodePython

def label_encode_train_test(train_values, test_values):
    """
    Input example: train = ['red','blue','red'], test = ['blue','green']
    Return: ([0, 1, 0], [1, -1])
    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 label_encode_train_test(train_values, test_values):
    """
    Input example: train = ['red','blue','red'], test = ['blue','green']
    Return: ([0, 1, 0], [1, -1])
    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

Inputtrain = ['red','blue','red'], test = ['blue','green']

Expected([0, 1, 0], [1, -1])

green was unseen during training, so it becomes -1.

Hidden Test Categories

empty or minimal inputduplicates or tieslarger input