# 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
