Retrain, or not Retrain? Online Machine Learning with Gradient Boosting

Training 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 variegated.