Easy
Machine Learning• Loss & optimization
Implement mean squared error
Given two lists of predictions and targets (same length), compute the mean squared error: (1/n) * sum((pred[i] - target[i])^2).
Input: Two lines. First: space-separated predictions. Second: space-separated targets.
Output: One number (MSE), rounded to 4 decimal places.
Example:
- Input:
1 2 3and1 2 4 - Output:
0.3333(only last element differs: (3-4)^2 = 1, mean = 1/3)
Sample Test Verification
2 Test CasesSample Input (stdin)
1 2 3 1 2 4
Expected Output (stdout)
0.3333
solution.py
Pyodide v0.26.4 WASM
Initializing Monaco Python Environment...
Input (stdin)
1 2 3
1 2 4
Expected Output (stdout)
0.3333