What are the best practices for preventing overfitting in your ML model?

In my experience, ensuring data quality, including completeness, accuracy, timeliness, consistency, validity, and uniqueness, is critical for successful data-driven projects. During one of my project's "marketing campaign analysis", incomplete customer profiles, inaccurate purchase records, outdated contact information, data inconsistencies, invalid entries, and duplicate records significantly impacted our analysis. Resolving these issues through data cleansing and validation improved the accuracy and reliability of our analyses. This underscored the need to prioritize data quality across all project stages, ensuring meaningful insights for informed decision-making.

According to my experience, I would like to give some insights: For healthcare data, employing regularized models like logistic regression, support vector machines with appropriate kernels, or tree-based models such as random forests is vital. These models handle complex relationships while mitigating excessive complexity and overfitting, ensuring accurate disease diagnosis and patient prognosis. In retail datasets, models like collaborative filtering, decision trees, and gradient boosting machines are effective in capturing customer behavior and purchasing patterns without overfitting. These models facilitate understanding customer preferences and trends, leading to better generalization for recommendation systems and sales predictions.

Surprisingly, "over parameterized" models can have better generalization than smaller models. This can be seen with transformer models that have far more trainable parameters than there are training data points. Despite the classical U-shaped risk curve which proposes a sweet spot between having too little parameters (leading to too weak of a model) and too many parameters (leading to over-fitting), we instead seem to see in many cases the double descent phenomenon where if we keep increasing the number of parameters, we will get even better generalization and test performance. It is debated why this double descent phenomenon occurs, but one main view is that gradient descent implicitly regularizes the model (the metric is debated).

L1 regularization, also known as Lasso regularization, adds the absolute value of the magnitude of coefficients to the cost function. On the other hand, L2 regularization, known as Ridge regularization, adds the square of the magnitude of coefficients to the cost function. Both techniques help in reducing the complexity of the model by penalizing large coefficients, thus preventing overfitting.

Choosing the appropriate value for k, the number of folds in K-fold cross-validation, depends on the size of your dataset and the trade-off between variance and bias in the model evaluation. Here are some general guidelines: Small datasets: For smaller datasets, choosing a higher value of k (e.g., 5 or 10) can help ensure that each fold has enough data for both training and testing, reducing the variance in the model evaluation. Large datasets: With larger datasets, a lower value of k (e.g., 5) can be sufficient. This choice can save computational time while still providing a reliable estimate of the model's performance.

This is one of the most important steps in ensuring your model does not overfit. Try the model on similar system and check its performance. For example, if you create a pump failure predictive system for water injection pump in the oilfield facilities, you want to check the model's performance for generator feed water pump to check whether the model is general enough to predict the failure of a water pump. Both pump systems are quite similar, thus provide a case for testing

Overfitting is the phenomena where your model performs better only on the trained/seen data and underperforms on unseen/new data. - Ensuring that our data has undergone proper processing like removal of noise, outliers, the data should have proper variance which can acheived by normalization and standarization methods. - Selection of relevant features will make the model learn better, pruning of correlated features etc. - Regularizing features by inducing penalty to increase model efficiency. - Ensembling: Combining weak learners to make the model learn efficiently. - Algo: Choosing the right algo, for eg: RF is a efficient ensemble method for ML, yet should not chosed in overfitting as it a bagging model.