how to calculate mape

Art Scpae

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09-10-2024

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Demystifying MAPE: A Comprehensive Guide to Calculating Mean Absolute Percentage Error

The Mean Absolute Percentage Error (MAPE) is a widely used metric to assess the accuracy of forecasting models. It measures the average percentage difference between predicted and actual values, offering a clear understanding of the model's performance in relative terms.

In this article, we'll delve into the intricacies of calculating MAPE, exploring its advantages and limitations, and providing practical examples for a deeper understanding.

Understanding the Fundamentals:

What is MAPE?

MAPE represents the average absolute percentage error between your forecast and the actual data points. It is expressed as a percentage and calculated as follows:

MAPE = (1/n) * Σ(|Actual Value - Forecasted Value| / |Actual Value|) * 100

Where:

• n: Number of data points
• Σ: Summation
• | |: Absolute value

Why Use MAPE?

• Relative Error: MAPE expresses error as a percentage of the actual value, providing a more intuitive understanding of the model's performance, especially when dealing with varying scales of data.
• Easy Interpretation: A lower MAPE value signifies a more accurate forecast, with 0% being a perfect prediction.
• Widely Applicable: MAPE is a versatile metric used across various fields, including finance, marketing, and operations.

Step-by-Step Calculation:

Let's illustrate the calculation of MAPE with a simple example:

1. Calculate the absolute error for each month:

   • January: |100 - 90| = 10
   • February: |120 - 110| = 10
   • March: |150 - 140| = 10

2. Calculate the absolute percentage error for each month:

   • January: (10 / 100) * 100 = 10%
   • February: (10 / 120) * 100 = 8.33%
   • March: (10 / 150) * 100 = 6.67%

3. Sum the absolute percentage errors:

   • 10% + 8.33% + 6.67% = 25%

4. Divide the sum by the number of data points (3 months):

   • 25% / 3 = 8.33%

Therefore, the MAPE for this example is 8.33%.

Advantages and Limitations of MAPE:

Advantages:

• Intuitive and easy to interpret.
• Widely used across various fields.
• Captures relative error, making it suitable for data with varying scales.

Limitations:

• Sensitive to outliers: A single outlier with a large absolute error can significantly skew the MAPE value.
• Zero Actual Values: MAPE is undefined when the actual value is zero. This can occur in scenarios with low demand or initial product launches.
• Not suitable for comparing models: MAPE is not directly comparable across different datasets with varying scales.

Practical Considerations and Alternatives:

• Outlier Handling: Employ robust methods like winsorization or trimming to mitigate the impact of outliers on MAPE calculations.
• Alternative Metrics: Consider using alternative error metrics like Symmetric Mean Absolute Percentage Error (SMAPE) or Root Mean Squared Error (RMSE) when dealing with potential zero values or comparing models across different datasets.

Conclusion:

MAPE is a valuable tool for evaluating the accuracy of forecasting models. However, it's crucial to be aware of its limitations and consider alternative metrics when appropriate. By understanding the nuances of MAPE and its application, you can gain deeper insights into the performance of your forecasting models and make more informed decisions.

Further Exploration:

For deeper insights into MAPE and its applications, explore resources like:

• GitHub repository: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_regression.py
• Kaggle Kernels: https://www.kaggle.com/

Note: This article has been compiled from various sources, including contributions from the GitHub community. We acknowledge and appreciate the insights and knowledge shared by developers and data scientists who contribute to open-source projects.