#0079

MAE and RMSE

EasyML15 min15 XP

Problem

Given y_true and y_pred lists, return a dictionary with mae and rmse.

Why This Matters

Metrics decide what model looks best. MAE is easy to interpret; RMSE punishes large errors harder.

Function Signature

def regression_metrics(y_true, y_pred):

Examples

Example 1

Input[3, 5], [2, 7]

Output{"mae": 1.5, "rmse": 1.5811...}

Errors are 1 and -2.

Constraints

Lists are the same non-zero length.
Return floats.
Do not round unless a caller asks.

CodePython

def regression_metrics(y_true, y_pred):
    """
    Input example: [3, 5], [2, 7]
    Return: {"mae": 1.5, "rmse": 1.5811...}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return {"mae": 0, "rmse": 0}

def regression_metrics(y_true, y_pred):
    """
    Input example: [3, 5], [2, 7]
    Return: {"mae": 1.5, "rmse": 1.5811...}
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return {"mae": 0, "rmse": 0}

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Two predictions

Input[3,5], [2,7]

Expectedmae 1.5 and rmse sqrt(2.5)

Absolute errors are 1 and 2.

Hidden Test Categories

perfect predictionsnegative valuesthree or more rows