The way in which academic software is developed differs starkly from the way that software is engineered in industry. In this article, I summarize the main differences between academic and professional software development and reveal how academics can up their game.
Forecasting is a powerful technique for time-series data. Here, I investigate the most common variants of forecasting algorithms: ARMA, ARIMA, SARIMA, and ARIMAX, which are primarily based on autocorrelation and moving averages.
Prediction and forecasting are similar, yet distinct areas for which machine learning techniques can be used. Here, I differentiate the two approaches using weather forecasting as an example.
ROC and precision-recall curves are a staple for the interpretation of binary classifiers. This post gives an intuition on how these curves are constructed and their associated AUCs are interpreted.
Inference is concerned with learning about the data generation process, while prediction is concerned with estimating the outcome for new observations. These contrasting principles are associated with the the generative modeling and machine learning communities. Here, I showcase the differences and similarities between the two concepts and offer insights about what the practitioners from both fields can learn from each other.
For multi-class prediction scenarios, we can use similar performance measures as for binary classification. Here, I explain how we can obtain the (weighted) accuracy, micro- and macro-averaged F1-scores, and a generalization of the AUC to the multi-class setting.
datascienceblog.net now exists already for more than one month. In this post I offer a look behind the scenes of the blog and show the progress that has been made with respect to content, features of the blog, SEO, and search traffic.
Linear discriminant analysis (LDA) is a classification and dimensionality reduction technique that is particularly useful for multi-class prediction problems. In this post I investigate the properties of LDA and the related methods of quadratic discriminant analysis and regularized discriminant analysis.
Bayesian modeling does not have to be tedious. Using probabilistic programming it is relatively easy to implement statistical models that make use of MCMC sampling. In this post, I explore probabilistic programming using Stan.
Performance measures for feature selection should consider the complexity of the model in addition to the fit of the model. Popular feature selection criteria are the adjusted R squared, the Cp statistic, and the AIC.