diff --git a/docs/source/basics/main_concepts.rst b/docs/source/basics/main_concepts.rst index 8c6e6ec631..c8119d99bd 100644 --- a/docs/source/basics/main_concepts.rst +++ b/docs/source/basics/main_concepts.rst @@ -5,7 +5,7 @@ The main framework concepts are as follows: - **Flexibility.** FEDOT can be used to automate the construction of solutions for various `problems `_, `data types `_ (texts, images, tables), and :doc:`models `; - **Extensibility.** Pipeline optimization algorithms are data- and task-independent, yet you can use :doc:`special strategies ` for specific tasks or data types (time-series forecasting, NLP, tabular data, etc.) to increase the efficiency; -- **Integrability.** FEDOT supports widely used ML libraries (Scikit-learn, CatBoost, XGBoost, etc.) and allows you to integrate `custom ones `_; +- **Integrability.** FEDOT supports widely used ML libraries (Scikit-learn, CatBoost, XGBoost, etc.) and allows you to integrate `custom ones `_; - **Tuningability.** Various :doc:`hyper-parameters tuning methods ` are supported including models' custom evaluation metrics and search spaces; - **Versatility.** FEDOT is :doc:`not limited to specific modeling tasks `, for example, it can be used in ODE or PDE; - **Reproducibility.** Resulting pipelines can be :doc:`exported separately as JSON ` or :doc:`together with your input data as ZIP archive ` for experiments reproducibility;