What is the best way to manage overfitting and underfitting in statistical validation for ML?

We can tell that our model is overfitting if the training error is decreasing monotonically, but the test error increases. This means that the trained model is no longer generalizing the data but rather also learning the noise. We can address overfitting by either increasing the dataset (since the model is too complex for the data) or adding a regularization parameter to penalize the weights and make the model less complex. On the other hand, we can recognize underfitting when the model is too simple and unable to learn the training data. One of the ways we can address this is by decreasing the value of the regularization parameter (if it's already in place) to add more complexity to the model.

Overfitting in machine learning happens when a model learns too much from its training data, including noise and specific details, but struggles to generalize to new data. It doesn't capture the underlying patterns effectively. Underfitting, on the other hand, occurs when a model learns too little from the training data, missing the complexity and variability of the data. It's too simplistic and doesn't make accurate predictions. Balancing between these two extremes is essential for building effective machine learning models.

When doing Feature Engineering, always validate the performance of your model with the new features on a separate validation set to ensure that the improvements are not due to overfitting. Having too many features means that some of them could introduce noise to the model which is bad because it leads to overfitting as the model learns from the noise rather than the true relationships.

When looking for overfitting, it's crucial to consider how you create your training and testing datasets. If your testing data is too similar to the training data, and the real-world scenario won't provide such similar data for predictions (like needing future data rather than a random sample for testing), this can be risky. You might not notice overfitting by just comparing error metrics, but it could become apparent when using a truly representative test set.

One way to address overfitting in CNNs is by using techniques such as dropout and data augmentation. Dropout randomly deactivates some neurones during training, preventing the network from relying too much on specific features. Data augmentation is a technique that applies random (but realistic) transformations to the images in your training set. For example, you can apply: - Geometric transformations (e.g. rotations, flips, or crops) - Colour space transformations (e.g. change RGB colour channels or intensify colours) - Kernel filters (e.g. sharpen or blur an image) As a results, you effectively increase the diversity of the training data and help the model to generalise better on unseen data. Try using OpenCV and let me know!

In a project I did recently involving medical handwritten recognition, one effective way I dealt with overfitting and underfitting was the use of data augmentation(because my dataset was small) and dropout regularisation. My model was a CNN-LSTM and I preprocessed the word images and performed data augmentation using opencv to create diverse forms of the images. Data augmentation steps included geometric transformations (rotation, flipping and rescaling) and kernel filters such as masking, blurring and contrast adjustment. The dropout regularisation was implemented in the model construction stage for both the CNN stage and the LSTM. I tried different dropout parameters and my model performed very well on both the training and test data.

In addition to traditional cross-validation and regularization methods, consider integrating Bayesian optimization for hyperparameter tuning. This approach can significantly enhance model performance by systematically and efficiently searching for the optimal set of hyperparameters. Unlike grid or random search, Bayesian optimization uses prior results to inform future trials, making the search process more targeted. It's particularly effective in finding the right balance between model complexity and prediction accuracy, addressing both overfitting and underfitting!

k-fold X-validation, is a practical example of a diagnostic tool for V & R! While building a predictive model, with k-fold X-validation, we divide the dataset into 'k' subsets or folds. Then, we train our model on 'k-1' folds & validate it on the remaining one. By repeating this process 'k' times, we get a set of 'k' performance scores. If our model's performance varies significantly between these folds, it could indicate overfitting. Now, on the other hand, regularization like L1(Lasso) might force some regression coefficients to be exactly zero, effectively eliminating them from the model, simplifying it. And L2 regularization, reduces the magnitude of all coefficients, preventing them from becoming too large & dominating the model.

Choosing the best validation method depends on the dataset size, available computational resources, and the specific characteristics of the machine learning task. Common validation methods include holdout validation, cross validation and stratified cross-validation. The choice depends on the specific requirements of the task and the need to balance computational efficiency with reliable performance estimates.

First & foremost, I think understanding of dataset's characteristics, size, & structure is crucial. Additionally, defining the problem type, whether it's classification, regression, or clustering, is fundamental to selecting an appropriate validation method. Btw, the dataset's size plays a pivotal role in the decision-making process. For large datasets, straightforward approaches like Hold-Out Validation or K-Fold Cross-Validation are often sufficient. However, smaller datasets require more specialized techniques like Leave-One-Out Cross-Validation. The data distribution also matters, particularly in classification tasks with imbalanced classes. For hyperparameter tuning cases, Nested Cross-Validation is a great option!

It is indeed very important to be mindful of the risks of overfitting and underfitting when developing and testing ML/AI models. Also after the models have been developed and tested, it is very important to keep monitoring them though. Models that have been deployed for instance need to be monitored for phenomena such as 'model drift', model degradation due to changes in external factors that impact the relationships between model inputs and outputs. Given the dynamic nature of ML/AI models and the environment they operate in, it is crucial to also have dynamic governance/oversight in place when models are deployed.

Overfitting is a significant issue that requires ongoing attention in model development and maintenance. This is especially true as the distribution of production data often changes. It's wise to remain pessimistic, assuming that models might/will fail, and to stay prepared to adapt them for unseen data. On another note, overfitting isn't always bad. In fields like physics, healthcare, drug discovery etc... there can be a need to learn very specific details, even from noise.