The concept of Overfitting and Underfitting in Machine Learning

The concept of Overfitting and Underfitting in Machine Learning

In the world of machine learning, building a model that performs well on both training and unseen data is often a delicate balancing act. Two common pitfalls that data scientists encounter are overfitting and underfitting. Understanding these concepts is crucial for creating robust models that generalize well to new data.

Overfitting

Occurs when a machine learning model learns the details and noise in the training data to an extent that it negatively impacts the performance of the model on new data. This happens when the model is too complex, capturing random fluctuations and outliers in the training dataset.

Example;

Imagine we are building a model to predict house prices based on various features like size, location, and number of bedrooms. If we include too many features, such as the specific color of the front door or the number of windows, the model might perform exceptionally well on the training data but poorly on new data. This is because it has learned the noise and peculiarities of the training set rather than the underlying patterns.

How to know if your model is overfitting

1. High accuracy on training data.
2. Low accuracy on validation or test data.
3. Large difference between training and validation/test performance.

How to Address Overfitting:

1. Simplify the Model

---> Reduce the complexity of the model by removing less important features or choosing a simpler algorithm.

2. Regularization

---> Techniques like Lasso (L1) and Ridge (L2) regularization add a penalty for larger coefficients in the model, discouraging it from fitting the noise.

3. Cross-Validation

---> Use cross-validation to ensure the model performs well on different subsets of the data.

4. Pruning (for decision trees)

---> Remove parts of the tree that do not provide power to classify instances.

Underfitting

This happens when a model is too simple to capture the underlying patterns in the data, in other words; A model which does not capture the underlying relationship in the dataset on which it's trained. This can result from using too few features, having an overly simple model, or insufficient training

Example;

Continuing with the house price prediction example, if we build a model using only one feature, such as the size of the house, it might be too simplistic. This model would fail to capture the influence of other important features like location, number of bedrooms, and age of the property.

How to know if your model is Underfitting:

• Low accuracy on both training and validation/test data.
• Similar performance on training and validation/test datasets.

How to Address Underfitting:

1. Increase Model Complexity

---> Add more features or choose a more complex algorithm that can capture intricate patterns.

2. Feature Engineering

---> Create new features that provide more information to the model.

3. Train Longer

---> Allow the model to train for more epochs or iterations, especially for complex models like neural networks.

Finding the Right Balance

The key to successful machine learning is finding the right balance between overfitting and underfitting. Here are some best practices:

1. Regularly Validate the Model

---> Use validation data to check the performance of your model during the training process.

2. Use Learning Curves

---> Plot learning curves to diagnose whether your model is overfitting or underfitting. Learning curves show the training and validation scores as functions of the training set size or training iterations.

3. Hyperparameter Tuning

---> Adjust hyperparameters using techniques like grid search or random search to find the optimal settings for your model.

4. Ensemble Methods

---> Combine predictions from multiple models to reduce the risk of overfitting and underfitting. Techniques like bagging, boosting, and stacking can improve model performance.