What do you do if your statistical predictions need a boost?

As a statistician aiming to stay organized, employing structured data management practices, utilizing version control systems, maintaining clear documentation, and categorizing data effectively are essential strategies. Collaborating with other professionals in machine learning can offer diverse perspectives, access to complementary expertise, opportunities for knowledge exchange, validation of models through peer review, and potential for interdisciplinary research breakthroughs. When analyzing time series data, employing visualization techniques such as line plots, scatter plots, and histograms can provide insights into trends and patterns.

Boosting statistical predictions can be achieved through several strategies. Firstly, refine the statistical model by reassessing its assumptions, parameters, and algorithms. Optimize hyperparameters or consider more sophisticated models if necessary. Feature engineering is another powerful tool, involving the creation or extraction of new features from existing data, enhancing predictive capabilities.

Opting for feature importance in model refinement is akin to a brewmaster relying on their expertise to determine the critical ingredients that define a beer's character. Just as a brewmaster knows the exact impact of each hop variety or malt type on the final brew, using feature importance allows you to discern which variables significantly influence your model's predictions. This method, akin to the brewmaster's skillful selection, ensures that only the most impactful features are included, enhancing the model's performance. By focusing on feature importance, you're distilling down to the essence of your data, ensuring your model is as nuanced and effective as a finely crafted beer.

Personally I do model selection first and variable selection last. PCA is ok but not always appropriate. Correlation matrices don't have any information about the magnitude of the effect and can be challenging to use if there are a lot of variables. I have always found that if I get the structure of the model 'right' (i.e. reflects reality) that I have cleaner results for the variables of interest. I also find out as much as possible about the nature and methodology used for collecting the variables. This is important for model building and for having some domain information to inform the process.

If your statistical predictions need enhancing, scrutinizing your model's complexity is a great start. A simplistic model may fail to capture the data's intricacies, causing underfitting. Conversely, a model that's too intricate risks overfitting, learning noise instead of the actual pattern. To strike the right balance, use techniques like cross-validation and monitor metrics like R-squared or RMSE. For instance, if an economic forecast model doesn't perform well, adjusting its complexity while closely watching these metrics can lead to more accurate and reliable predictions.

I personally look for the model that accurately reflects the physical reality of the inherent structure of the data and/or the sampling methodology used to collect it as step one of the modeling process. For example primary education achievement variables are usually in hierarchical model of student in class in school in school district etc. As far as criteria. No don't use r-squared. If you must then at least use the adjusted r-squared which accounts for the number of variables in the model. RMSE is not particularly relevant anymore as many of the most used models use MLE, not OLS based estimates. Only use p-values for initial cut. smaller p-value NE larger effect size. Do use pareto standardized parameters or delta of -2LL for keeping.

Boosting your statistical predictions often requires fine-tuning your algorithms' hyperparameters. Each model has unique settings that, when optimally adjusted, can significantly enhance performance. For example, in a machine learning model like a random forest, tweaking the number of trees or tree depth can yield better results. Employ methods like grid search or random search to systematically explore and identify the most effective parameter combinations. This process is akin to precision-tuning an instrument, ensuring your model performs at its best.

If your statistical predictions need enhancement, consider ensemble methods. These techniques, like bagging, boosting, or stacking, merge multiple models to bolster prediction accuracy. Ensemble methods aim to reduce both variance and bias. For instance, a random forest—an ensemble method—aggregates numerous decision trees, leading to more reliable and accurate predictions than any single tree could provide. This approach is like assembling a team of experts rather than relying on a single individual's judgment, combining their strengths to achieve a more robust outcome.

You should investigate different algorithms designed to tune your parameters (input variables) which would improve the results of all the models you are testing including ensemble models.

As a statistician, staying organized involves structured data management, version control, clear documentation, and effective data categorization. Collaborating with machine learning experts provides diverse perspectives, knowledge exchange, and validation opportunities. Analyzing time series data benefits from visualization methods like line plots and statistical techniques such as autocorrelation analysis and forecasting models like ARIMA for accurate predictions.

The previous points made are the most important steps to take when optimizing the predictive power of your system. If you have additional computing power left and your ensemble isnt taking that long to predict, there are some steps you can take to get a little edge: - In a similar fashion to bagging, you can gather the top N best hyperparameter model instances and average their predictions, or in a weighted fashion, based on their predictive power. This of course takes N times as long, but it will slightly decrease overfitting and improve the system in most cases. - If any of your ensemble components are neural networks, you can use PGD or other similar adversarial training algorithms to significantly increase the robustness of these models

One of the most important skills is to see what is NOT on the page. I have seen very talented data scientists try to “torture the data until it confesses” Often internal transactional and behavioral datasets need to be supplemented with external data like Axiom, D&B and Experian. When I am seeking better predictions usually there is one or more explanatory variables missing. Ask yourself? What data would I like to have that I don’t? Go find a source… and then advanced data science kicks in.

Do Feature Engineering: In my experience, almost all the time, feature engineering helps to boost the accuracy of the model. Especially understand the business context and convert that as features

To boost statistical predictions, consider techniques like feature engineering, ensemble methods, parameter tuning, cross-validation, model selection, data augmentation, regularization, feature selection, advanced models, and seeking consultation from experts.