Place | Competition | Description | Solution |
---|---|---|---|
1st | 2024 AutoML Grand Prix | Team "LightAutoML testers" | 1 stage, 3 stage, 4 stage, 5 stage |
demo0.py
- building ML pipeline from blocks and fit + predict the pipeline itself.demo1.py
- several ML pipelines creation (using importances based cutoff feature selector) to build 2 level stacking using AutoML classdemo2.py
- several ML pipelines creation (using iteartive feature selection algorithm) to build 2 level stacking using AutoML classdemo3.py
- several ML pipelines creation (using combination of cutoff and iterative FS algos) to build 2 level stacking using AutoML classdemo4.py
- creation of classification and regression tasks for AutoML with loss and evaluation metric setupdemo5.py
- 2 level stacking using AutoML class with different algos on first level including LGBM, Linear and LinearL1demo6.py
- AutoML with nested CV usagedemo7.py
- AutoML preset usage for tabular datasets (predefined structure of AutoML pipeline and simple interface for users without building from blocks)demo8.py
- creation pipelines from blocks to build AutoML, solving multiclass classification taskdemo9.py
- AutoML time utilization preset usage for tabular datasets (predefined structure of AutoML pipeline and simple interface for users without building from blocks)demo10.py
- creation pipelines from blocks (including CatBoost) to build AutoML, solving multiclass classification taskdemo11.py
- AutoML NLP preset usage for tabular datasets with text columnsdemo12.py
- AutoML tabular preset usage with custom validation scheme and multiprocessed inferencedemo13.py
- AutoML TS preset usage with lag and diff transformers' parameters selectiondemo14.py
- Groupby features (using TabularAutoML preset and custom pipeline)