Key Points on Overfitting, Underfitting and Good Fitting in Machine Learning for Beginners

September 12, 2023September 12, 2023 Editor

Overfitting and underfitting are two common but critical issues in machine learning. These affect the performance and generalization of a model. They relate to how well a model fits the training data and how it performs on unseen data (validation or test data). Before we move to ‘Good Fit’ let’s understand and explore these concepts in more detail:

What is Overfitting in Machine Learning?

Overfitting occurs when a machine learning model learns the training data too well, to the point that it captures noise and random fluctuations in the data instead of the underlying patterns. The overfitted model characterised with low bias and high variance.

What are the traits of overfitting models?

The model’s performance on the training data is excellent, with low training error.
However, the model’s performance on unseen or validation/test data is poor, with high validation/test error.
The model has learned to memorize the training data rather than generalize from it.
The model might have an excessively complex structure, such as too many parameters or features.

How to overcome or prevent overfitting?

Reduce model complexity by simplifying the architecture (e.g., using fewer layers in a neural network).
Collect more training data if possible.
Apply regularization techniques (e.g., L1 or L2 regularization) to penalize overly complex models.
Use techniques like cross-validation to assess model performance more robustly.

What is Underfitting in Machine Learning?

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data, leading to poor performance on both training and validation/test data.

Wat are the traits of underfitting Machine Learning models?

The model’s performance on the training data is subpar, with a high training error.
The model’s performance on unseen or validation/test data is also poor, with high validation/test error.
The model may not be expressive enough to represent the underlying relationships in the data.

How to overcome or prevent underfitting?

Increase model complexity by adding more layers, features, or parameters.
Use a more complex model architecture that is better suited to the data (e.g., using a deep neural network for complex tasks).
Engineer relevant features that provide more information to the model.
Consider using different algorithms or hyperparameter settings.

Balancing between overfitting and underfitting is a fundamental challenge in machine learning. The goal is to find the right level of model complexity that allows the model to generalize well to unseen data. Techniques such as hyperparameter tuning, cross-validation, and monitoring learning curves can help in finding the optimal trade-off between these two issues.

What is Good Fit in Machine Learning?

A “good fit” or optimal model performance is all about creating a model that generalizes well to unseen data while effectively capturing the underlying patterns in the training data. Here are some key characteristics and considerations for achieving good fitting in machine learning:

Low Training Error and Low Test Error:

A well-fitted model should have low training error, indicating that it fits the training data well.

It should also have low test (validation or test set) error, demonstrating that it generalizes well to new, unseen data.

Balanced Bias and Variance:

Good fitting often involves finding a balance between bias and variance. Bias represents errors due to overly simplistic assumptions, while variance represents errors due to model complexity.

A model with high bias and low variance may underfit the data, while a model with low bias and high variance may overfit the data.

The goal is to find the right level of complexity that minimizes both bias and variance, resulting in a good fit.

Validation and Cross-Validation:

Validate the model’s performance using a separate validation set or through cross-validation techniques.

Cross-validation helps assess how well the model generalizes across different subsets of the data, providing a more robust evaluation.

Regularization:

Apply regularization techniques like L1 or L2 regularization to prevent overfitting by penalizing complex models.

Regularization helps in achieving a good fit by reducing the risk of overfitting while maintaining model performance.

Feature Engineering:

Carefully select and preprocess features to provide meaningful information to the model.

Feature engineering can improve the model’s ability to capture relevant patterns in the data.

Hyperparameter Tuning:

Experiment with different hyperparameter settings (e.g., learning rate, batch size, number of layers, etc.) to find the configuration that results in the best model performance.

Hyperparameter tuning is often an iterative process that helps achieve a good fit.

Monitoring Learning Curves:

Visualize learning curves to understand how the model’s performance evolves over time as it’s trained on more data.

This can help diagnose underfitting or overfitting issues and make necessary adjustments.

Ensemble Methods:

Consider using ensemble methods like bagging, boosting, or stacking to combine multiple models for improved performance and generalization.

Domain Knowledge:

Incorporate domain knowledge and expertise into the model-building process, as it can guide feature selection and model architecture decisions.

Evaluate Business Objectives:

Ultimately, the definition of a “good fit” depends on the specific problem and business objectives. A good fit aligns with the desired outcome and meets the project’s goals.

Achieving a good fit in machine learning often involves an iterative and systematic approach that combines data preprocessing, model selection, hyperparameter tuning, and evaluation. It’s important to continually refine and improve the model until it meets the desired performance and generalization criteria for the given task.