How can you avoid overfitting in your ML project?

The pitfall of a one-off split into training, validation and test sets is that it can lead to overfitting to the specific random split of data. Nested cross-validation, on the other hand, is a more robust approach. It overcomes this pitfall by repeatedly splitting the data into training, validation and holdout sets during inner and outer cross-validation loops. In the inner loop, it optimizes model configurations (e.g., hyperparameter tuning, feature selection) on various validation sets. The outer loop then evaluates the model's generalization error on multiple holdout sets. This robust approach ensures a more reliable performance assessment and reduces overfitting.

Based on my experience, data plays a pivotal role in machine learning algorithms. It's essential to enrich the training dataset by employing various techniques like data augmentation to encompass a wide range of data types. Ensuring randomness and diversity are maintained across all aspects of your datasets is equally important.

1) Use a larger training dataset. The more data you have, the less likely your model is to overfit the training data. 2) Use regularization techniques. Regularization techniques add a penalty to the model's complexity, which can help to prevent overfitting. 3) Use early stopping. Early stopping stops training the model before it has a chance to overfit the training data. 4) Use cross-validation. Cross-validation evaluates the model on multiple held-out datasets, which can help to identify overfitting. 5) Use ensemble methods. Ensemble methods combine multiple models to produce more accurate predictions. This can help to reduce overfitting, as individual models are less likely to overfit the training data.

The test set, or held-out set, should be first to be created and representative of data the model sees. Think of it as a golden dataset and it should only be used for benchmarking. It need not be class-balanced. Training and validation sets are ones which the model sees and you are free to split data between the two however you want to, intending to maximize performance on test set. Validation set is used to optimize model performance and tune model hyperparameters (e.g. YOLO models), so the train-val split must be done meticulously. Use every possible information that you can to stratify data. For example, if you have images from different classes with timestamps, the train-val split can be stratified on both class and time (day, hour).

Something to be conscious of when doing this is to ensure your test and validation datasets are independent from the training data. For my dissertation, for example, I did a piece on de-noising EEG signals. My data consisted of several trials for the same individuals across a number of scenarios, so I had to look at splitting on unseen records, unseen scenarios and unseen individuals. In this way I trained the model using number of splitting variations. When splitting only on unseen records, the same individuals on the same scenario appeared at random in my test and validation datasets reducing the independence of those sets and not giving a true reflection of the models ability to generalise.

Try to reduce model complexity and overfitting. Use cross-validation to assess your model's performance on multiple subsets of the data. Cross-validation helps you identify whether your model generalizes well or is overfitting.

Overfitting is a huge issue in training a machine learning model as it reduces the ability of the model to generalize and learn efficiently. With overfitting, the model only learns and captures the attributes of the training data. Below are the common ways by which we can reduce overfitting: 1. Regularization of weights and features of the given data to reduce variance. 2. Feature selection: Correlation among features is a parameter that needs to be considered and independent features which are highly correlated should be penalized. 3. Augmentation of data to capture generalized behavior of real-world scenario

When we are fortunate to possess an abundant amount of data related to the process we wish to model, we can utilize techniques like K-fold-cross-validation to ensure that our algorithms are exposed to a multitude of scenarios. This approach is particularly critical in scenarios where data collection is not only expensive but also limited, as seen in industrial shop floors. K-fold-cross-validation proves invaluable in this respect. It helps in training and evaluating models robustly with the available data, reducing the risk of overfitting and ensuring that the model generalizes well to unseen data.

Regularization is a key technique in machine learning to prevent overfitting. It introduces constraints or penalties to the model, reducing its complexity. This is crucial as overly complex models tend to "learn" the noise in training data, failing to generalize well for new data. Techniques like L1 and L2 regularization adjust the model's weights, simplifying the model while retaining its predictive power. Regularization is versatile, applicable to various models from linear to logistic regression, and even in complex neural networks. It effectively reduces model variance, enhancing its ability to generalize.

This is something we use on a regular basis. Not just to reduce the risk of overfitting, but to meet the data minimisation requirements of GDPR - using the least amount of customer data required. Generally for feature reduction, the rule of thumb is that you should limit your feature set to only those features with an importance above 0.1. Additionally, you can check for autocorrelation between variables as highly correlated variables provide no additional information. A simple method I have used to great effect for reducing features is to conduct EDA to understand the relationship between proposed features and the target variable - including only those that exhibit a statistically significant difference across the target classes.

Reducing features in your machine learning project can help prevent overfitting by simplifying the model's representation of the data. When there are too many features, the model may start to learn noise or irrelevant patterns, which can lead to overfitting. By selecting a smaller set of the most relevant and informative features, you reduce the model's complexity, making it more focused on the relationships in the important features. This simplification limits the model's capacity to fit noise, improves its ability to perform well on new, unseen data, and enhances its overall robustness.

Choose relevant features and remove irrelevant ones. Reducing the dimensionality of your data can help prevent overfitting. Feature selection is the process of choosing a subset of relevant features (variables or attributes) from a larger set of data features for use in machine learning models. There are a few techniques you can use, but understanding the domain and problem you're working on is crucial. Domain expertise can help identify which features are likely to be important.

Feature reduction is used very often in practice. Mostly you either don't need as many features as originally though or you are even legally required (GDPR, HIPPA, client-confidentiality, etc.) to use as fwe identifiable features as possible. Reducing space complexity is usually a direct win.

There are tons of ways to avoid overfitting your model. The best solution depends on your specific model and overfitting problem, but some common techniques are - Cross-validation such as K-fold cross-validation - Simpler models, adding too many features to your model might introduce overfitting on your training data - Feature engineering - You can engineer new features that are more relevant to the model in an effort to reduce the number of features you feed your model.

Data augmentation helps reduce overfitting by increasing the effective size of your training dataset and introducing diversity into the data that your model learns from. This expanded dataset provides more opportunities for the model to learn the underlying patterns and relationships in the data, which can help it generalize better to unseen data. Data augmentation acts as a form of regularization. It forces the model to be more robust and less sensitive to minor variations in the input data. When the model encounters augmented examples during training, it learns to recognize the same underlying patterns in multiple variations of the data, reducing its tendency to overfit to specific training instances.

Data plays a pivotal role in building ML models. While more data is better for training models and that the testing error reduces monotonically as you increase examples until it reaches pure error, one simply cannot keep adding the same kind of data to improve model performance. Seeing too much of the same kind of data also makes the model overfit to it. Variety in data matters! Good quality dataset comprises open-source data if available and your custom/private data. Augmentation in data should be such that it's relevant to the problem being solved. For example, while training a Person Detection model using YOLO, a vertical flip augmentation may not be helpful, as the model will never see inverted people during inference.

Small datasets are more prone to overfitting because they provide a limited amount of information for the model to learn from. Overfitting occurs when a model learns to fit the training data too closely, capturing noise and random fluctuations rather than the underlying patterns or relationships. A popular strategy is to combine multiple models to leverage their strengths and compensate for individual weaknesses for instance Bagging (Bootstrap Aggregating) used in Random forest, boosting used in XGBoost, and stacking methods often reduce overfitting.

Data augmentation is a vital strategy to combat overfitting, especially when dealing with small or imbalanced datasets. Essentially, it involves creating new, synthetic data based on your existing data, enhancing diversity and volume. This process allows the model to learn from a wider array of examples, focusing on more general characteristics rather than memorizing specific details. For images, this could mean flipping, rotating, or adding noise, while for text, it might involve generating new sentences or altering words. By enriching the dataset in this way, data augmentation helps the machine learning model achieve better generalization.

There are great libraries and plugins that do that like albumentation or neptune. Use these techniques but don't reinvent the wheel :)

Overfitting is often related to the complexity of the model. There is a need to strike a balance between model complexity and data fitting.

Overfitting occurs when a model fits the training data too closely but generalizes poorly to new data. To avoid overfitting: >use larger training sets, >regularization techniques like L1/L2, >early stopping based on validation set performance, >data augmentation through transformation, and >cross-validation across different splits of the training data. Simplicity of Model - Also choose an appropriate model complexity - overly complex models tend to overfit more. Following these best practices helps optimize model generalization. By following these tips, we can avoid overfitting in our machine learning project and improve the performance of our model on real-world data which may come after training & validation of ML models.

Always try to understand the problem you are solving from first principles. Try to understand what the contributing factors are to trim down the model. In my experience, simpler models perform better and they are less computationally intensive. Good pruning and complexity space reductions go a long way!

Things to consider for model evaluation to avoid overfitting include: Cross-Validation: Use techniques like k-fold and LOOCV to assess model generalization. Evaluation Metrics: Employ F1 score, ROC AUC, and log-likelihood for deeper insights. Ensemble Learning: Combine models with bagging, boosting, and stacking. Bias Detection: Perform fairness audits and disparate impact analysis. Adversarial Validation: Test on similar but different data. Regularization: Apply L1, L2, Elastic Net, or weight decay. Hyperparameter Tuning: Optimize using Bayesian, grid, or random search. Model Selection Criteria: Consider AIC (Akaike’s Information Criteria), BIC (Bayesian Information Criteria), and MDL (Minimum Description Length) to prevent overfit model

Evaluation is a bit of science and a bit of art. A confusion matrix usually gives you a good intuition of where your model is good and where it's bad. Use this intuition to go back to the dataset and to the training method to make adjustments. Also, you are usually better of comparing your model to an existing benchmark than trying to establish your own. Benchmarking is extremely tricky! Don't forget to have fun with it ;) Play around with the model, change stuff, and break things! This is the way you develop an intuition for different models and their components.

Since data is critical, it would be helpful if we think of ways in which existing data could be pre-processed such that the it effectively represents the underlying pattern. Some specific examples for this could be handling outliers in financial models, frequency filtering of sensor signals to remove noise, removal of stop words in NLP tasks, grouping customers appropriately under customer behavior analysis.

- Instead of training from scratch, fine-tune a trained model for a generic problem. - Add a class of "irrelevant inputs" and force the model to correctly recognize and classify them.

The bias-variance trade-off is central to this process. High bias (underfitting) occurs when a model is too simple to capture the underlying patterns, while high variance (overfitting) happens when a model is too complex and fits noise. Finding the right balance is key. Regularisation techniques and hyperparameter tuning help strike this balance by controlling model complexity.

An important aspect to consider in ML is the DIVERSIFICATION of data sources. This is particularly vital in the context of industrial shop floors, where machinery from various manufacturers with differing specifications is commonplace. The data collected from these machines can exhibit substantial variations, even when the underlying processes are consistent. Therefore, it is crucial to adopt a strategy that is agnostic to these disparities, ultimately paving the way for successful industrial deployment.

The term 'overfitting' often implies that the model is too finely tuned to the training data, capturing its noise and irregularities. Yet, there's another challenge: the training data itself might not always provide adequate clues about the true underlying distribution, especially regarding the correct positioning of inflection points. In such cases, even if the model seems to fit the training data well, it might be a 'misfitting' when it comes to representing the real problem curve!