What is overfitting and why should you avoid it in your machine learning models?

Your teacher teaches you some problems in the class. You practice the exact same problems very hard. In exam, you can't solve a single slightly modified problem. Reason? Overfitting! Model works very well on training but the performance worsens on testing data. How to avoid overfitting - 1. Cross Kfold 2. LOOCV 3. Add dropout 4. Regularisation

Overfitting in machine learning occurs when a model learns the training data too well, capturing noise or random fluctuations in the data rather than the underlying patterns. As a result, the model performs well on the training data but poorly on unseen data, failing to generalize.

Overfitting is when a model performs well on the train set (for instance, gives high accuracy) but underperforms on the test set. Generally, if the model's performance metric drops >10% on the test set, it can be considered an overfit. However this may depend on the domain and could vary. Why to never deploy an overfit model? 1. It's because the model will be no good for data other than the train set, including the test set and any future, unseen data. 2. An overfit model is unsustainable and may immediately result in highly inaccurate predictions upon deployment. It's simply not reliable and robust.

Data always includes a mix of signal and noise. A good model should fit the signal, but not the noise. When a model fits the noise as well - that's overfitting!

Imagine you're trying to teach a child to distinguish between different breeds of dogs by showing them pictures. If you only use a few specific images to train the child, they might mistakenly believe that all dogs of a certain breed have identical features as those in the limited set of pictures. So, if you show them a picture of a different dog of the same breed but with slightly different characteristics, they might struggle to recognize it correctly. By this, I mean to say the limited set of pictures represents the training data, and the child's difficulty in recognizing new dogs accurately reflects the model's overfitting, as it hasn't learned to generalize beyond the specific examples it was trained on.

Overfitting in machine learning models occurs when a model learns the training data too closely, including its noise and outliers, leading to poor generalization on new, unseen data. It's characterized by high accuracy on training data but low accuracy on testing or validation data. To detect overfitting, monitor the model's performance on both the training and a separate validation dataset. A significant performance gap indicates overfitting. Avoiding overfitting is crucial because it ensures the model remains flexible and applicable to broader, real-world data, thereby improving its utility and reliability in making predictions or decisions based on new inputs.

Detecting overfitting is like checking if your clothes fit properly. One way is using test set of data that model hasn't seen before. If your model performs well on both the training data and test data, it's probably not overfitting. If it does great on training but poorly on test, that's a red flag. Another trick is using techniques like cross-validation, where you train your model on different chunks of data and test it on rest. If it consistently performs well across different splits, you're in clear. Monitoring performance metrics like accuracy or loss during training can give clues—if your model's performance on the training data keeps improving while its performance on new data stalls or worsens, you might have an overfitting problem.

Scenario: you built a model on training data (supervised learning) and saw that the model was able to predict 98% of the labels/results perfectly. You're happy now! Then, you used the same model to predict something you didn't tell the model during the training. And now the model could predict only 77% of the labels/results. Why a huge gap between train and test results? What happened is, your model memorized the information you gave and instead of learning the generic pattern, it memorized the data (kind of like an open-book test). But when asked a question that was not in the book, based on the concepts only, it started giving wrong answers. So, you want your model to understand the concepts instead of cramming the given data.

Existem diversas técnicas para detectar overfitting, sendo a validação cruzada uma das mais utilizadas no mundo real (pela minha observação). A validação cruzada envolve a divisão do dataset em várias partes, ou "folds". O modelo é então treinado em várias combinações desses folds e testado no fold restante, permitindo uma avaliação mais robusta da performance do modelo em dados não vistos anteriormente. Essa abordagem nos ajuda a ganhar confiança no processo de modelagem, pois fornece uma estimativa mais precisa da capacidade do modelo de generalizar para novos dados.

The most effective method to gauge a model's performance is by analyzing the plot that illustrates the decrease in training and validation loss. Typically, overfitting is indicated when the training loss continues to decline, while the validation loss begins to increase. This divergence serves as a clear sign that the model may be overfitting; in other words, it's learning the specific details of the training data rather than the underlying patterns.

Avoid this common mistake: do not clean your data before splitting it into training, test, and validation sets. Doing so can cause your test data to "borrow" information from the training set.

Preventing overfitting is like finding the right balance in a recipe—you want your model to learn without getting too obsessed with details. Simplify your model, get more data if possible, or use regularization techniques to penalize complexity. Validate your model on separate data and consider techniques like dropout to prevent overreliance on specific features. With these strategies, you'll build a model that generalizes well to new data.

To prevent overfitting in machine learning, employ techniques such as cross-validation, which uses multiple data splits to ensure the model generalizes well. Regularization methods like L1 and L2 add penalties on large coefficients to reduce complexity. Pruning removes unnecessary features or decision tree branches. Dropout methods for neural networks randomly ignore nodes, making the model less sensitive to specific data features. Additionally, simplifying the model by reducing the number of layers or parameters can help, as can increasing the dataset size to provide more generalization examples. These strategies help create robust models that perform well on new, unseen data.

Incorporating strategies such as cross-validation, dropout, and regularization is undoubtedly beneficial. Implementing early stopping can also help in identifying the optimal point before the validation loss begins to rise. However, an often overlooked yet simple approach is to adjust the model's size—both in terms of layers and parameters—to match the complexity of the task at hand. Essentially, models designed for simpler tasks should inherently be less complex.

To prevent or mitigate overfitting; • Use simpler models: Start with simple models and increase complexity only if necessary. Simpler models are less prone to overfitting. • Cross-validation: Use cross-validation techniques to ensure the model performs well on unseen data. • Pruning: For decision trees, pruning back branches can help prevent the model from learning noise in the training data. • Dropout: In neural networks, dropout is a technique where randomly selected neurons are ignored during training, which helps in preventing overfitting by making the neural network less sensitive to the specific weights of neurons.

It is usually assumed that a model is overfit if it performs worse than expected on new data that was not seen by the model during training. It's important to question whether or not the model was actually expected to generalize to the new data. In medicine, regulated AI devices have an indication for use, and that clarifies the circumstances and population in which the model is expected to apply. I think the concept of "indication for use" would be valuable beyond regulated health products.

To address overfitting, you can try several techniques: Cross-validation: Split your data into training and validation sets, and use techniques like k-fold cross-validation to assess model performance. Fature selection: Choose a subset of relevant features to train the model, discarding irrelevant or redundant ones. Ensemble methods: Combine multiple models like bootstrap, regression analysis with multilayer feedforward neural network in order to reduce overfitting. Techniques like bagging and boosting can help improve generalization by combining the predictions of multiple models. Simplify the model architecture: Use simpler models with fewer parameters, such as reducing the number of layers or neurons in a neural network.

What: In layman terms when your model is just learning the data (Byhearting) without actually understanding or extracting the essence out of it is called Overfitting Why Bad: Overfit model will not perform well in current/new data because of "What". How to handle: Look into the data and understand its complexity if you give simple data to complex model it will overfit. If you let the model branch too much it will overfit. Always check the difference in performance on train and test and take appropriate measures

In machine learning, overfitting is like asking a group of people to explain a movie after watching it in a cinema. Each person will tell the story in their own way, using different words, sequence, and pace. Similarly, in machine learning, the training data set represents these different versions of the story. If you give too much importance to one person's version, the model becomes overfitted to that specific data. Just like no single person's version perfectly represents the movie, no individual data point perfectly captures the underlying patterns in the data. However, by considering multiple perspectives and making an informed judgement, the model can learn to generalise better.