Striking the Balance: How to Avoid Underfitting & Overfitting in ML Models

Creating a good model is about finding the right balance. If the model is too simple, it misses important patterns (underfitting). If it’s too complex, it memorizes the data and doesn’t work well on new data (overfitting). In this edition, we’ll show you how to find the sweet spot in the bias-variance trade-off for better models.

To help you learn, we’ve included some questions that will make you think about how to use the bias-variance trade-off in real-life scenarios. These exercises will help you apply these concepts and improve your skills.

The Story of Two Extremes: Underfitting & Overfitting

Underfitting:

Imagine heading into an exam without studying and only knowing the basics—just enough to get by. That’s underfitting.

Underfitting happens when a model is too simple to capture the complexities in the data, leading it to miss the key patterns needed for accurate predictions.

Indicator of underfitting: The model is consistently inaccurate on both training and test data.

Real-World Analogy: Underfitting is like trying to fit a straight line to data that clearly follows a curve. No matter how hard you try, it just won’t work because the model isn’t flexible enough.

Example: Predicting house prices using only square footage is underfitting. While square footage matters, you’re ignoring other important factors like location, the number of bedrooms, or amenities—all of which influence the price. This results in inaccurate predictions.

Overfitting:

Imagine you’re taking a pop quiz. You memorize all the answers, but you don’t actually understand the material. You get a perfect score—not because you’ve learned the subject, but because you’ve memorized the exact questions. That’s overfitting.

Overfitting occurs when your model becomes too complex and starts learning every tiny detail or noise in the training data, treating these specifics as if they were general patterns. As a result, the model performs exceptionally well on the training data but fails to generalize to new, unseen data.

Indicator of overfitting: High accuracy on training data but poor performance on test data.

Real-World Analogy: It is like memorizing the answers to last year’s test without understanding the material. When the test changes, you are completely lost.

Example: Imagine training a model to predict house prices based on square footage, but your dataset includes an outlier—a tiny apartment with an unusually high price because of its prime location. The model might learn to overestimate the price of small homes in other areas, leading to inaccurate predictions for typical houses in less expensive neighborhoods.

Bias and Variance in Machine Learning

When building machine learning models, two key sources of error are bias and variance. Understanding how they interact is crucial for creating accurate models.

• In machine learning, bias refers to the difference between the expected prediction of a model and the true value we are trying to predict. It quantifies how far off, on average, the model’s predictions are from the target values. A model with high bias fails to capture the underlying patterns in the data, often leading to underfitting.
• Variance, on the other hand, measures how much a model’s predictions would change if it were trained on different subsets of the training data. Essentially, it gauges the model's sensitivity to the variations in the data. A model with high variance is prone to overfitting, which leads to poor performance on unseen data.

As illustrated by Hastie, Tibshirani, and Friedman (2009), the bullseye diagram below offers a useful visualization to clarify these concepts:

• The center of the target represents perfect predictions.
• Each blue point corresponds to a model’s prediction for a specific test point (x, y) based on a different training subset.

In the diagram:

• Variance is represented by the spread of the points around the target center. A wider spread indicates higher variance, meaning the model’s predictions change significantly with different training sets.
• Bias is indicated by the location of the point cluster relative to the center. A cluster far from the center suggests high bias, while a cluster close to the center suggests low bias.

The Goal: Minimize both bias and variance to build a model that is both accurate and able to generalize well to new data.

As model complexity increases-

• Bias decreases: The model becomes better at capturing the underlying relationships in the data, leading to more accurate predictions.
• Variance increases: The model becomes more sensitive to fluctuations and noise in the training data, which increases the risk of overfitting.

The Golden Ratio of Machine Learning

Finding the right balance between bias and variance is crucial for our model’s success. Here’s how to think about it:

Underfitting: High Bias + Low Variance

Overfitting: Low Bias + High Variance

Optimal (Good Fit): Low Bias + Low Variance

How to Achieve the Bias-Variance Trade-off?

1. Start Simple

Always begin with a simple model. If it works well, great! If not, gradually increase complexity. Start with linear regression, and if the data suggests more complexity, explore decision trees or neural networks.

2. Regularization: Control Complexity

Regularization techniques like L1 and L2 penalize overly complex models, helping reduce overfitting. They’re perfect for keeping your model’s complexity in check.

3. Cross-Validation

Use k-fold cross-validation to ensure your model performs well on different data splits. This helps prevent the model from perfectly memorizing the training data and ensures it generalizes well to new data.

4. Ensemble for Stability

Techniques like bagging and boosting (e.g., Random Forests, XGBoost) combine multiple models, which reduces the risk of overfitting and helps your model become more stable.

5. Early Stopping in Neural Networks

In deep learning, early stopping stops training once the model's performance on a validation set starts to degrade. This prevents the model from memorizing the training data and becoming overfitted.

In conclusion, understanding the balance between bias and variance is crucial for building effective models. As model complexity increases, bias tends to decrease while variance increases. Striking the right balance between the two is key to avoiding underfitting and overfitting—ensuring that your model not only fits the training data well but also generalizes effectively to new, unseen data. By adjusting the model complexity, you can improve performance and build models that are both accurate and reliable.

Data Sapient Quiz:

1. You are applying K-means clustering to a dataset with 100 data points. After running the algorithm with different values of k, you observe the following:

With k = 1, all the data points are grouped into a single cluster.

With k = 100, each data point becomes its own cluster.

Which of the following statements is true about the impact of k = 1 and k = 100 on the clustering model?

A) k = 1 may lead to overfitting, while k = 100 will lead to underfitting, as each data point is perfectly fit into its own cluster.

B) k = 1 leads to underfitting because the model oversimplifies the data, while k = 100 leads to overfitting because the model creates too many clusters, potentially capturing noise.

C) k = 1 and k = 100 both lead to underfitting, as both settings fail to capture the complexity of the data.

D) k = 1 and k = 100 both lead to overfitting, as both extreme values of k will perfectly fit the data but fail to generalize.

2. In the context of neural networks, which of the following techniques is most effective in preventing overfitting?

A) Decreasing the network’s depth (hidden layers) to reduce the number of parameters.

B) Using a learning rate that is too high, speeding up convergence.

C) Adding dropout layers that randomly turn off neurons during training.

D) Using a very small batch size to increase the stochasticity of the gradient updates.

3. You are applying the K-Nearest Neighbors (KNN) algorithm to a classification problem. Which of the following best describes the behavior when k = 1?

a) k = 1 makes the model less sensitive to outliers, improving test accuracy.

b) k = 1 makes the model overly sensitive to noise, leading to overfitting.

c) k = 1 improves generalization, resulting in better accuracy on the test set.

d) k = 1 overfits, but increasing k improves generalization.

Free Response Question:

1. How can you tell if your model has a high bias or high variance problem?
2. Can you explain cross-validation’s role in addressing the bias-variance tradeoff?
3. Would it be better if an ML algorithm exhibits a greater amount of Bias or a greater amount of Variance?

That’s all for today!

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