Reducible Error vs Irreducible Error in Machine Learning
Last Updated :
23 Jul, 2025
There are two types of errors in machine learning model. They are reducible and irreducible error. We cannot reduce the irreducible error as it is caused by factors that are beyond our control, but we can reduce the reducible error since it arises from factors that we can manage or improve.
Machine Learning Error
Total Error that occur when a model do prediction is:
Total Error = Bias2 + Variance + Irreducible Error
Irreducible Error
Irreducible error is the error in a machine learning model that cannot be reduced, as it is due to unknown or uncontrollable factors. Irreducible error is caused by unknown or uncontrollable variables and is inherent in the data.
y=f(x)+\varepsilon
- Y: The actual outcome you want to predict.
- X: The input features.
- f(X): The true underlying relationship between x and y.
- \varepsilon: The noise or irreducible error randomness in data that can’t be predicted or removed.
Irreducible ErrorThe graph shows that even if we build the perfect model to predict house prices based on size, the predictions will never be 100% accurate. That’s because there are always some things we just can’t account for like sudden market changes, noise in the data, or hidden factors like the house’s condition or neighborhood vibe. These unpredictable influences are called irreducible error.
Reducible error
Reducible error is the error in a machine learning model that can be reduced, as it is due to known or controllable factors. Reducible error is caused by known and controllable variables. Reducible errors are caused by bias and variance in the model's predictions, and they can be minimized by improving the model's complexity, using better algorithms, or tuning hyperparameters.
Reducible Error (\varepsilon) = Y - f(X)
Reducible Error
In machine learning, reducible and irreducible error are two main component of total error prediction. Reducible error arises from issues like high bias high variance and can be minimized by improving the model, tuning hyperparameters, or selecting better features. In contrast, irreducible error is caused by unknown, random, or uncontrollable factors such as noise in data, measurement errors, or hidden variables that no model can predict, no matter how advanced.
Comparison Between Irreducible Error and Reducible Error
Irreducible Error | Reducible Error |
|---|
Error that cannot be reduced by improving the model's data. | Error that can be reduced by improving the model's data. |
|---|
Due to random noise or unmeasurable variables in the data. | Due to bias and variance in the model. |
|---|
Cannot be controlled. | Can be controlled by techniques like model tuning, feature engineering, etc. |
|---|
Sets a lower bound on the total error achievable. | Affects how well the model can learn from data. |
|---|
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