Data Science

RMSE (Root Mean Squared Error)

Arun Murali

Tags: data-science; evaluation; regression; rmse

🟪 1-Minute Summary

RMSE measures the average magnitude of prediction errors, penalizing large errors more heavily due to squaring. Formula: √(Σ(actual - predicted)² / n). Same units as target variable. Lower is better. Use when large errors are particularly bad (e.g., price prediction). More sensitive to outliers than MAE.

🟦 Core Notes (Must-Know)

Formula

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Interpretation

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When to Use RMSE

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RMSE vs MAE

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🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

  1. “Why does RMSE penalize large errors more than MAE?”

    • [Answer: Squaring amplifies large errors]

  2. “Is lower RMSE always better?”

    • [Answer framework to be filled in]

🟥 Common Mistakes (Traps to Avoid)

Mistake 1: Comparing RMSE across different scales

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🟩 Mini Example (Quick Application)

Scenario

[Calculate RMSE for house price predictions]

Solution

from sklearn.metrics import mean_squared_error
import numpy as np

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