What are the most important considerations when selecting a statistical learning method?

Type of data often dictates what your options realistically are. If your dataset is images, well, you aren't going to use a linear regression. You'll be forced to work with neural nets, probably a CNN. If you're working with tabular data, random forests or XGBoost/CatBoost/LightGBM are a safe bet.

Having a super complex, high performance model is not always good. If you cannot explain how your model works, you may have a hard time convincing professionals from other departments into supporting it. Maintaining an ML model takes time, effort and money. You need to help others see that your model is valuable and reliable enough to be worth it.

The way to balance the interpretability and the predictive power of the model comes down to the purpose for the model. Generally, if you’re looking to prescribe what effect input variables have on the target variable, going with regression is best. If you wanted to make your regression more flexible and predictive, you could add in non-linear transformations (log, square, etc…), splines, and interaction variables and use step-wise regression to select the best versions, although this will make your model a little harder to interpret. Using ML algorithms are ideal if you want to get as much predictive power as possible. A mentor recommended creating a regression that predicts the ML’s predictions as an interpretable proxy for it!

The trade-off between complexity and interpretability in AI models presents a challenging balance. Complex models, like ANN or ensemble methods, have many parameters and intricate structures capable of capturing complex patterns. They typically achieve higher accuracy. However, the complexity makes these models less transparent and harder to interpret. The simpler models, such as decision trees, have simpler structures and fewer parameters. These models are easier to understand and explain. However, they are inefficient in modeling complex patterns, leading to lower accuracy. Interpretability is vital in areas where understanding the decision-making process is crucial. Complexity might be favored where predictive accuracy is crucial.

A small addition based on experience. A model that is pretty good in terms of complexity and not tough to interpret is —— XGBoost. Yes, you might not know this, XGBoost being an ensemble approach is targeted towards optimization, hence, better results. And also while it is more complex than maybe Linear Regression, it is super simple than a Neural Network ??

With increases in consumer concern and regulation of AI systems, explainability becomes critical for building model trust. For model-driven decisions involving approving or denying an individual a resource, there is growing need to explain to that individual why they were denied or what they could do differently to be approved. Some models, such as regressions and decision trees, are naturally interpretable. The structure of the model explains how inputs affect the model's output. Other models, such as random forests, gradient boosting, and neural networks, require additional work to help explain how the model makes the prediction. Techniques such as feature importance, counter-factual, PD, LIME, and Shapley values help interpret models.

Bias refers to the error due to overly simplistic assumptions in the algorithm. High bias can cause an algorithm to miss the relevant relations, leading to underfitting. For example, if a linear regression model is used for a non-linear problem, it will fail to capture the actual relationships. Variance refers to the error due to too much complexity in the algorithm. High variance can make the model learn from the random noise in the training data rather than the actual trend, leading to overfitting. For example, if a complex decision tree creates a branch for every slight variation in the training data, it captures noise rather than the actual trend. The optimal model complexity is balancing bias and variance to minimize total error.

Remember the rule of thumb: Low bias + high variance = Overfitting High bias + Low variance = Underfitting And you want to avoid both.

Before building your model, it is critical that data scientists understand how the model will be used. Will the model be scoring a table in batch or used to process ad-hoc transactions? What scoring speeds does the model need to achieve? Transactional ad-hoc scoring typically requires sub-second latency. For batch processing of tables or databases, scoring may need to complete in minutes or hours (depending on the scale of the data), but not days. The more complex the model and data preprocessing/postprocessing steps, the longer the model will take to score. While modern model serving architecture allows computational scaling to improve model scoring speed, this may not always be available depending on the use case.

When selecting statistical models, if you have the opportunity to deploy multiple, then it is something to consider. While there might be 1 method that optimizes all key performance indicators, others can also provide perspective for analysts. For example, while finding the arithmetic mean, or average, may not reduce the variance as much as other methods, it's an important factor in many statistical analysis and is often a variable in other methods. Adding the mean can also highlight how much more variance the optimized methods capture.

It would also be helpful to know if your data is labeled or not that would decide if your method would be supervised or unsupervised. Your size of data in terms of breadth and depth would also decide the methods you can use. But at the end your business objectives override everything. What you want to take out from the data, how does business want to interpret results is most important. In a recent project, we had to build a Prediction model for order anomalies and we did not use ML algorithms because there was no labeled data and available options in neural networks lacked interpretability.