Cerliani

Simple yet Effective Evolutions of Recursive Method without the need for Deep LearningPhoto by Halfcut Pokemon on UnsplashWhen working on a time series forecasting problem, a standard benchmarked approach is the recursive one. It can be easily used on top of any machine learning model, it requires low assumptions, and it’s easily explainable.Recursive forecasting consists in creating lagged features of the target series and fitting a machine learning model on them. When forecasting further steps in the future, the…

Comparing Refit Strategies for Continuous Learning in Scikit-LearnPhoto by VD Photography on UnsplashTraining a machine learning model requires energy, time, and patience. Smart data scientists organize experiments and track trials on the historical data to deploy the best solution. Problems may arise when we pass newly available samples to our pre-build machine learning pipeline. In the case of predictive algorithms, the registered performances may diverge from the expected ones.The causes behind discrepancies are…

Alerting Meaningful Multivariate Drift and ensuring Data QualityPhoto by FLY:D on UnsplashModel monitoring is becoming a hot trend in machine learning. With the crescent hype in the activities concerning the MLOps, we register the rise of tools and research about the topic.One of the most interesting is for sure the Confidence-based Performance Estimation (CBPE) algorithm developed by NannyML. They implemented a novel procedure to estimate future models' performance degradation in absence of ground truth. It may yield…