#0120

Threshold Probabilities

EasyML15 min10 XP

Problem

Given probabilities and threshold, return 1 when probability >= threshold else 0.

Why This Matters

Classification thresholds change precision, recall, and business behavior. The default 0.5 is not always best.

Function Signature

def threshold_probs(probabilities, threshold):

Examples

Example 1

Input[0.2, 0.5, 0.8], threshold=0.5

Output[0, 1, 1]

The threshold is inclusive.

Constraints

Return the requested value exactly.
Handle the stated edge cases.
Keep the solution readable.

CodePython

def threshold_probs(probabilities, threshold):
    """
    Input example: [0.2, 0.5, 0.8], threshold=0.5
    Return: [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 threshold_probs(probabilities, threshold):
    """
    Input example: [0.2, 0.5, 0.8], threshold=0.5
    Return: [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

Input[0.2, 0.5, 0.8], threshold=0.5

Expected[0, 1, 1]

The threshold is inclusive.

Hidden Test Categories

empty listthreshold 0threshold 1