Bias and Variance In Machine Learning Understanding the Trade-Off

5 min readJun 11, 2024

In the realm of Machine Learning creating a model that generalize well to new, which is unseen data is very important, and its the ultimate goal of every ML engineer. To understand what Bias and Variance is we will go through some real life example, Visual representation and at last in this story we will discuss how to overcome these challenges.

Bias and Variance are two fundamental concepts in machine learning that requires careful consideration when building models.

What is Bias?

Technically Bias is the error between the model predictions and the ground truth. Further more, it describes that how will the model matches the training dataset.

Signs of High Bias and High Variance in a Model

The image has been taken from bmc.com

Example:

The best example of Bias in ML model is in Health Care that is “computer-aided diagnosis (CAD) systems have been found to return lower accuracy results for black patients than white patients.”

Now lets talk about High Bias and Low Bias and will distinguish them from each other.

High Bias:

Lets understand High Bias with a real life example. “ Let us consider you are a Teacher and you are trying to predict that how will your students do on upcoming exams. Here’s how High Bias can play out:

High Bias Scenario:

You as a teacher decided that all the students will get an average marks, regardless of what they studied. This is your assumption (High Bias). In reality some students may work hard, some didn't and it will reflect their grades.

This is like High Bias model in machine learning, that make strong assumptions about training data (all students are average) but wasn’t able to capture the important factors in data (students individual study habits). So, thus it performs poorly on unseen data (upcoming result).

The Result: Underfitting the model performs poorly on training data (your students: study performance) and new unseen data (exam grades), because it wasn’t able to learn the intricacies of how well each student performs.

Conclusion of High Bias:

High Bias in a machine learning model occurs when the model makes strong assumption on the data but because of the simplicity of the model it wasn’t able to capture the underlying hidden patterns from data and thus it leads us to underfitting which can cause poor performance on both training and unseen data.

What is Variance:

In machine learning variance refers to how well is a model specific to the training data it’s given. Or, we can say that “ The changes in the model when using different portions of data.

High Variance:

Lets say you have a bunch of students again but this time you’re a teacher who use a fancy app to predict their grades.

High Variance: Let’s say the app focuses on very specific details from past assignments, like how often a student uses a particular function on the calculator app. This might work well for the training data (past assignments), but it wouldn’t capture the bigger picture (overall study habits) and might not work well for unseen data (future exams). Small changes in the training data (different assignments) could lead to big changes in the predictions (grades). This is like a model with high variance.

It memorizes the specifics of the training data a little too well and struggles to adapt to new situations.
This can lead to overfitting, where the model performs well on the training data but poorly on unseen data.

The Bias-Variance Trade-Off

The crux of creating a good machine learning model lies in finding the right balance between bias and variance. This balance is crucial for minimizing the total error, which can be decomposed into three parts:

Bias²: Error due to overly simplistic assumptions in the learning algorithm.
Variance: Error due to too much complexity in the learning algorithm.
Irreducible Error: Error that cannot be reduced by any model, caused by noise in the data.

Managing the Trade-Off

Model Selection: Choosing the right model complexity is key. Simple models (like linear regression) have high bias but low variance, while complex models (like deep neural networks) have low bias but high variance.
Cross-Validation: Techniques like k-fold cross-validation help in assessing the model’s performance and understanding the trade-off between bias and variance.
Regularization: Techniques such as L1 (Lasso) and L2 (Ridge) regularization add a penalty for larger coefficients, effectively controlling variance and helping prevent overfitting.
Ensemble Methods: Combining multiple models (bagging, boosting) can reduce variance without significantly increasing bias, leading to better generalization.

Real-Life Case Study: Predicting Diabetes

Let’s consider a real-life example of predicting the onset of diabetes based on diagnostic measures.

High Bias Scenario: Using a simple logistic regression model with only a few features like age and weight might result in high bias. This model could miss out on other important predictors like blood pressure, insulin levels, and genetic factors, leading to underfitting.
High Variance Scenario: Using a complex model like a deep neural network with many layers and neurons might capture all details in the training data, including noise. If not properly regularized, this model might perform extremely well on the training set but poorly on new patients, indicating overfitting.
Balanced Approach: Employing a regularized logistic regression model with a carefully selected set of features can strike a balance. Additionally, techniques like cross-validation and ensemble methods can be used to ensure the model generalizes well to new data.

Conclusion

Understanding bias and variance is fundamental to the field of machine learning. Striking the right balance between these two can significantly enhance the performance of a model, ensuring it generalizes well to new, unseen data. As models become more complex and datasets grow larger, this trade-off remains a critical consideration for data scientists and machine learning practitioners.

By carefully managing bias and variance, one can develop robust models that not only fit the training data well but also perform admirably on new data, thereby achieving the ultimate goal of machine learning: creating predictive models that generalize well.