GBDTE
It's quite hard to navigate the channel, so I created this navigation/summary post. It's about a pet project I started about ten years ago. The main idea is that we can use slightly modified gradient boosted decision trees to both group objects and find trends for these groups.
📈beginning - the very first picture, the whole idea
📈credit scoring - problem statement, temporal instability
📈dataset - dataset preparation, ytzaurus vs Oracle
📈Vanilla GBDTE - experiment with math in instant view
📈Small MSE Dataset - the first approach to synthetic dataset for MSE GBDTE
📉Extracting components - how to get perfect components from chaotic signal
📉Leafs and components - check tree leaves and plot components
📉Evil of defaults - a debugging session, culprit - default parameters
📉Big MSE dataset - scatterplot with more clear "Gradient Boosting" message
📉LogLoss dataset - non-stationary dataset for binary classification
🎲Experiment on LogLoss dataset - first approach for running the algorithm on the dataset
🎲bad results - a very important mistake! Why you shouldn't use interpolation factors as extrapolating ones
🎲illustration for unstable class - a picture for a presentation
🎲learning curves LogLoss - learning curves for LogLoss case (non-stationary binary classification)