Examples

1. demo0.py - building ML pipeline from blocks and fit + predict the pipeline itself.
2. demo1.py - several ML pipelines creation (using importances based cutoff feature selector) to build 2 level stacking using AutoML class
3. demo2.py - several ML pipelines creation (using iteartive feature selection algorithm) to build 2 level stacking using AutoML class
4. demo3.py - several ML pipelines creation (using combination of cutoff and iterative FS algos) to build 2 level stacking using AutoML class
5. demo4.py - creation of classification and regression tasks for AutoML with loss and evaluation metric setup
6. demo5.py - 2 level stacking using AutoML class with different algos on first level including LGBM, Linear and LinearL1
7. demo6.py - AutoML with nested CV usage
8. demo7.py - AutoML preset usage for tabular datasets (predefined structure of AutoML pipeline and simple interface for users without building from blocks)
9. demo8.py - creation pipelines from blocks to build AutoML, solving multiclass classification task
10. demo9.py - AutoML time utilization preset usage for tabular datasets (predefined structure of AutoML pipeline and simple interface for users without building from blocks)
11. demo10.py - creation pipelines from blocks (including CatBoost) to build AutoML, solving multiclass classification task
12. demo11.py - AutoML NLP preset usage for tabular datasets with text columns
13. demo12.py - AutoML tabular preset usage with custom validation scheme and multiprocessed inference