Overfitting & Underfitting
— better understanding of model generalization
Overfitting
It is a phenomenon in deep learning where a machine learning model performs very well on the training data but fails to generalize well on unseen or test data. It occurs when the model becomes too complex and starts to memorize the training data rather than learning the underlying patterns or features.
Let’s consider an example of image classification. Suppose we have a dataset containing images of cats and dogs, and our goal is to train a deep-learning model to classify these images correctly. We split the dataset into training and test sets, with 80% for training and 20% for testing.
During training, the model repeatedly receives batches of images and adjusts its parameters to minimize the loss and improve its performance. As the training progresses, the model learns to recognize various cat and dog features, such as shapes, textures, and colors, and generalizes these patterns to make accurate predictions.
However, if the model becomes too complex or has too many parameters, it can start to overfit the training data. It may memorize the specific features of each individual image in the training set, including noise or irrelevant details, instead of learning the generalizable patterns that define a cat or a dog.
When tested on unseen images from the test set, the overfit model may not generalize well and may perform poorly. It may incorrectly classify images that visually resemble cats and dogs but differ in terms of lighting conditions, backgrounds, or poses since it has not learned to generalize beyond the specific training images.
How to prevent overfitting:
These techniques can help in reducing the model’s complexity and improving its generalization ability, which leads to preventing overfitting.
1. Use more data: Increasing the size of the training dataset generally helps to reduce overfitting. A larger dataset provides more diverse examples for the model to learn from, making it more likely to capture the underlying patterns of the data.
2. Split the data wisely: It’s important to split the data into three parts — training, validation, and test sets. The training set is used to train the model, the validation set is used to tune the hyperparameters and monitor the model’s performance, and the test set is used to evaluate the final performance of the model on unseen data. This separation allows us to detect overfitting and fine-tune the model accordingly.
3. Regularization techniques: Regularization helps to prevent overfitting by adding a penalty term to the loss function during training. This discourages the model from relying too heavily on certain weights or parameters. L1 and L2 regularization are commonly used techniques in deep learning. L1 regularization introduces a sparsity constraint, encouraging some weights to become exactly zero, while L2 regularization encourages small weights by adding a squared term to the loss function.
4. Dropout: Dropout is a regularization technique that randomly sets a fraction of the input units or connections to zero during each training step. This helps to prevent neurons from co-adapting and forces the model to learn more robust and generalizable features.
5. Data augmentation: Data augmentation involves applying random transformations to the training data, such as rotations, translations, flips, or zooms. This artificially increases the diversity of the dataset and helps the model to learn more invariant and generalized features.
6. Early stopping: Early stopping involves monitoring the model's performance on a validation set during training. If the model’s performance on the validation set starts to degrade after an initial improvement, training is stopped, preventing the model from overfitting. This helps to find the optimal point where the model has learned generalizable features.
7. Model architecture: Simplifying the model architecture can also help in avoiding overfitting. Reducing the number of layers, neurons, or parameters, makes the model less likely to memorize the training data and forces it to focus on the most important features.
Underfitting
It is the opposite of overfitting in deep learning. It occurs when a machine learning model is too simple or cannot capture the underlying patterns and complexities of the data. As a result, the model performs poorly on both the training and test data.
Let’s consider an example of image classification again. Suppose we have a dataset of cat and dog images, and we train a deep-learning model with a very basic architecture that has too few layers and parameters.
During training, the model is not able to learn the intricate features that distinguish cats from dogs. It fails to capture important patterns, such as shapes, textures, or color variations that are crucial for accurate classification. The model may make generic predictions, such as always classifying an image as a cat or a dog without considering the actual characteristics of the image.
As a consequence, the model suffers from underfitting. It is not able to capture the complexity of the data and fails to generalize well. Even on the training data, the model may exhibit a high loss or low accuracy.
When tested on unseen images from the test set, the underfit model continues to perform poorly, showing low accuracy and incorrect classifications. It is unable to identify the distinguishing features of different animals and cannot make accurate predictions.
How to prevent underfitting:
To address underfitting in deep learning, several strategies can be employed:
1. Increase model complexity: The model architecture can be modified to add more layers, neurons, or parameters to increase its capacity to learn and represent complex relationships in the data.
2. Fine-tune hyperparameters: Hyperparameters such as learning rate, batch size, and regularization strength can be adjusted to find the optimal configuration for the model. Experimenting with different settings can help improve the model’s performance.
3. Gather more data: Increasing the size of the training data can provide the model with more diverse examples and increase its chances of learning the underlying patterns.
4. Use more advanced models: Instead of using a basic or simple architecture, one can opt for more advanced models such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) that are designed to capture complex patterns in data.
5. Remove noise or outliers: The presence of noisy or outlier data points in the training set can negatively impact the model’s ability to learn. Removing or properly handling such data points can improve the model’s performance.
Conclusions:
- Finding the right balance between model complexity and generalization is crucial in deep learning. It requires optimizing the model’s capacity to learn from the training data without overfitting or underfitting. Regular monitoring, experimentation, and adjustment of various techniques and approaches can help achieve this balance and develop powerful and generalized models.
- By understanding and mitigating the risks of overfitting and underfitting in deep learning, practitioners can build models that effectively learn from data and make accurate predictions on unseen instances, contributing to the advancement of various fields such as computer vision, natural language processing, and data analytics.