Difference Between Underfitting and Overfitting in Machine Learning

Summary: Underfitting and overfitting are common issues in machine learning. Underfitting results from a model being too simple, while overfitting comes from a model being too complex. Balancing these ensures better generalization and accuracy.

Introduction

Machine learning empowers the machine to perform the task autonomously and evolve based on the available data. However, while working on a Machine Learning algorithm, one may come across the problem of Underfitting or overfitting. 

Both these aspects can impact the performance of the Machine Learning model. Hence, in this blog, we are going to discuss how to avoid Underfitting and overfitting.

What is Underfitting in Machine Learning?

Training data plays an important role in deciding the effectiveness of an ML model. However, any error or flaw can impact the overall analysis. In the case of Underfitting training data, the model is not able to establish a correlation between the input and output variables.

Underfitting results primarily because the model is too simple to work on the available data, and hence the training time escalates. It may require more input features. Hence, it is not able to deduce the right outcomes resulting in flawed output.

What is Overfitting in Machine Learning?

Unlike Underfitting, in the case of Overfitting, the Machine Learning model is too advanced or has too much complexity. Thus, impacting the output. A Machine Learning professional would encounter more cases of Overfitting as compared to Underfitting. 

However, an Overfitting ML model can work on data but produces less accurate output because the model has memorized the existing data points and fails to predict unseen data. Hence, an overfitted model is not something that you should be looking at.

The best way to overcome the Underfitting issue is to focus on increasing the duration of training or by adding accurate inputs. Most of the time, to avoid the Underfitting issue, the ML expert ends up adding too many features to it, leading to Overfitting. 

It may result in low bias but high variance. It means that the statistical model fits closely against the training data. And hence it is not able to generalize the new data points.

Identifying Overfitting can be difficult because the training model performs with higher accuracy than an Underfitting model. In the next segment, we will be highlighting the strategies that will help you address the issue of Underfitting and Overfitting.

How to Avoid Overfitting in Machine Learning? 

Overfitting is a common challenge in machine learning that occurs when a model learns the noise in the training data rather than the actual pattern. This leads to poor performance on new, unseen data. To ensure your machine learning model generalizes well, it’s essential to implement strategies that prevent overfitting. 

Below are some effective techniques to avoid overfitting.

K-fold Cross Validation

K-fold cross-validation is a powerful technique to mitigate overfitting. In this method, the dataset is divided into ‘k’ subsets or folds. The model is trained on ‘k-1’ folds and tested on the remaining fold. 

This process is repeated ‘k’ times, with each fold serving as the test set once. The performance metrics are then averaged to provide a more reliable estimate of the model’s performance.

By using K-fold cross-validation, you ensure that each data point is used for both training and testing. This helps in identifying if the model is overfitting to a particular subset of data and allows adjustments to improve generalization.

Regularisation

Regularisation introduces a penalty term to the loss function, discouraging the model from fitting the training data too closely. Two common types of regularization are L1 (Lasso) and L2 (Ridge).

• L1 Regularisation (Lasso): Adds the absolute value of the magnitude of coefficients as a penalty term to the loss function. This can lead to sparsity in the model parameters, effectively performing feature selection by shrinking some coefficients to zero.
• L2 Regularisation (Ridge): Adds the squared magnitude of coefficients as a penalty term. This prevents the coefficients from becoming too large, thus reducing model complexity and overfitting.

Regularisation ensures that the model remains simple and generalizes well to new data.

Feature Selection

Feature selection involves choosing only the most relevant features for your model. Reducing the number of features helps in decreasing the model’s complexity and the risk of overfitting. There are various methods for feature selection:

• Filter Methods: These use statistical techniques to evaluate the relevance of each feature. Examples include correlation coefficients and Chi-square tests.
• Wrapper Methods: These involve selecting features based on model performance. Techniques like Recursive Feature Elimination (RFE) fall under this category.
• Embedded Methods: These perform feature selection during the model training process. Regularization methods like Lasso can automatically select features by shrinking less important ones to zero.

By focusing on the most important features, you can create a more robust model that performs better on unseen data.

Combine Different Methods

Ensemble methods combine multiple models to improve predictive performance and reduce overfitting. Some popular ensemble techniques include:

• Bagging (Bootstrap Aggregating): Involves training multiple instances of the same model on different subsets of the training data and averaging their predictions. Random Forest is a well-known example of bagging.
• Boosting: Sequentially trains models, each trying to correct the errors of its predecessor. Examples include AdaBoost and Gradient Boosting.
• Stacking: Combines multiple models by training a meta-model to make the final prediction based on the outputs of the base models.

By leveraging the strengths of different models, ensemble methods can significantly enhance model performance and robustness.

How to Avoid Underfitting in Machine Learning?

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance on both the training and test sets. This issue can be mitigated through several strategies. Here, we’ll explore three effective methods: reducing regularisation, changing the model architecture, and adding more features to the data.

Reduce Regularisation

Regularisation techniques are commonly used to prevent overfitting by penalizing model complexity. However, excessive regularization can lead to underfitting, where the model becomes too simplistic. To avoid this, consider reducing the strength of regularization.

• Adjust Regularization Parameters: If you’re using L1 or L2 regularization, decrease the penalty term to allow the model more flexibility in fitting the data. For example, in ridge regression (L2 regularization), lower the alpha value.
• Balance Regularisation: Aim for a balance between overfitting and underfitting. Experiment with different regularisation strengths to find the optimal value that captures the data patterns without being overly complex.

Change the Model Architecture

Sometimes, the architecture of your model might be too simplistic to capture the intricacies of the data. Switching to a more complex model can help in such cases.

• Move to Non-Linear Models: If you’re using a linear model, consider switching to a non-linear model like decision trees, random forests, or support vector machines (SVMs) with non-linear kernels. These models can capture complex relationships that linear models might miss.
• Use Ensemble Methods: Methods like random forests and gradient boosting combine multiple models to improve predictive performance. These ensemble methods can reduce the risk of underfitting by leveraging the strengths of different models.
• Deep Learning Models: For large datasets with complex patterns, consider using deep learning models. Neural networks with multiple layers (deep networks) can learn intricate patterns in data, reducing the likelihood of underfitting.

Add More Features

The simplicity of the training data can also lead to underfitting. Enhancing the dataset by adding more features can help the model capture the underlying patterns more effectively.

• Feature Engineering: Create new features from the existing data that might provide additional information to the model. For instance, if you’re working with time-series data, adding features like moving averages, lags, or trend components can improve model performance.
• Polynomial Features: Transform the features to polynomial terms to allow the model to capture non-linear relationships. For example, if you’re predicting house prices, include not just the square footage but also the square of the square footage.
• External Data: Integrate additional relevant data from external sources. For example, if you’re predicting sales, consider adding economic indicators, weather data, or social media trends.

Summary of Difference between Underfitting and Overfitting

From the above discussion, we can conclude that both Underfitting and Overfitting are two common challenges in ML. Thus, it can majorly impact the performance and accuracy of the model. The contributing reason for the same is the complexity of the model, which refers to the degree to which a model can capture patterns in the data.

In the case of Underfitting, the model is too simple to identify significant patterns, whereas, in the case of Overfitting, the model is too complex, leading to too much noise in the data, thus, nullifying the generalization.

Both Underfitting and Overfitting lead to poor generalization and high-test error. Consequently, the ML model is not able to give accurate predictions. 

In order to achieve a good balance between these two problems, it’s important to select a model with an appropriate level of complexity that can capture the underlying patterns in the data while avoiding fitting too closely to the noise.

Frequently Asked Questions

What is underfitting in machine learning?

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. This leads to poor performance on both training and test sets. The model fails to learn the relationship between input and output variables, resulting in inaccurate predictions.

How can I avoid overfitting in machine learning?

To avoid overfitting, use techniques like K-fold cross-validation, which ensures the model generalizes well across different subsets of data. Regularization methods like L1 and L2 add penalty terms to reduce complexity. Feature selection and ensemble methods, such as bagging and boosting, also help in preventing overfitting.

What are the key differences between underfitting and overfitting?

Underfitting happens when a model is too simple and fails to capture data patterns, leading to high bias and poor performance. Overfitting occurs when a model is too complex, capturing noise instead of actual patterns, resulting in high variance and poor generalization to new data.

Closing Thoughts

The above discussion highlights the key difference between Overfitting and Underfitting. Both these issues can impact the performance of the ML model, and hence it becomes significant to carefully evaluate the data and use the right model architecture that can help in accurate output.

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If you have any further questions on Overfitting or Underfitting, drop your comments, and our experts will address them soon.