stackgbm

A minimalist implementation of model stacking by Wolpert (1992) doi:10.1016/S0893-6080(05)80023-1 for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.

Last 30 days

This package has been downloaded 452 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 47 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 6,062 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Apr 30, 2024 with 62 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.